{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "psychological-inquiry",
   "metadata": {},
   "source": [
    "### 加载、查看、去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "gentle-allergy",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "israeli-surface",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3683, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = pd.read_csv('./lagou2020.csv')\n",
    "job.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dominican-reality",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyShortName</th>\n",
       "      <th>city</th>\n",
       "      <th>companySize</th>\n",
       "      <th>education</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>industryField</th>\n",
       "      <th>salary</th>\n",
       "      <th>workYear</th>\n",
       "      <th>hitags</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>job_detail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>拉勾网</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>25k-35k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...</td>\n",
       "      <td>[\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]</td>\n",
       "      <td>\\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>OK Group</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>B轮</td>\n",
       "      <td>金融</td>\n",
       "      <td>25k-45k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]</td>\n",
       "      <td>\\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>金山办公软件</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>金山办公软件</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"免费班车\",\"免费体检\",\"地铁周边\"]</td>\n",
       "      <td>[\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]</td>\n",
       "      <td>\\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  positionName companyShortName city companySize education financeStage  \\\n",
       "0      高级数据分析师              拉勾网   北京   500-2000人        本科        D轮及以上   \n",
       "1        数据分析师         OK Group   北京   500-2000人        大专           B轮   \n",
       "2      高级数据分析师           金山办公软件   北京     2000人以上        本科         上市公司   \n",
       "3        数据分析师           金山办公软件   北京     2000人以上        本科         上市公司   \n",
       "4        数据分析师             京东集团   北京     2000人以上        本科         上市公司   \n",
       "\n",
       "  industryField   salary workYear  \\\n",
       "0          企业服务  25k-35k    5-10年   \n",
       "1            金融  25k-45k    5-10年   \n",
       "2         移动互联网  15k-25k     3-5年   \n",
       "3         移动互联网  15k-25k     1-3年   \n",
       "4            电商  15k-30k     3-5年   \n",
       "\n",
       "                                              hitags  \\\n",
       "0  [\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                             [\"免费班车\",\"免费体检\",\"地铁周边\"]   \n",
       "\n",
       "                companyLabelList  \\\n",
       "0  [\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]   \n",
       "1   [\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]   \n",
       "2  [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "3  [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "4  [\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]   \n",
       "\n",
       "                                          job_detail  \n",
       "0  \\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...  \n",
       "1  \\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...  \n",
       "2  \\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...  \n",
       "3  \\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...  \n",
       "4  \\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "twenty-beach",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['北京', '上海', '深圳', '广州', '杭州', '成都', '南京', '武汉', '西安', '厦门', '长沙',\n",
       "       '苏州', '天津'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.city.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "embedded-cream",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3507, 12)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.drop_duplicates(inplace=True)\n",
    "job.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "packed-growth",
   "metadata": {},
   "outputs": [],
   "source": [
    "#去重后行索引一定有缺失，所以重新设置一下\n",
    "job.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "underlying-resource",
   "metadata": {},
   "source": [
    "### 过滤非数据分析岗位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "comic-husband",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "5        True\n",
       "6        True\n",
       "7        True\n",
       "8        True\n",
       "9        True\n",
       "10       True\n",
       "11       True\n",
       "12       True\n",
       "13       True\n",
       "14       True\n",
       "15       True\n",
       "16       True\n",
       "17       True\n",
       "18       True\n",
       "19       True\n",
       "20       True\n",
       "21       True\n",
       "22       True\n",
       "23       True\n",
       "24       True\n",
       "25       True\n",
       "26       True\n",
       "27       True\n",
       "28       True\n",
       "29       True\n",
       "        ...  \n",
       "3477    False\n",
       "3478    False\n",
       "3479    False\n",
       "3480    False\n",
       "3481    False\n",
       "3482    False\n",
       "3483    False\n",
       "3484    False\n",
       "3485    False\n",
       "3486    False\n",
       "3487    False\n",
       "3488    False\n",
       "3489    False\n",
       "3490    False\n",
       "3491    False\n",
       "3492    False\n",
       "3493    False\n",
       "3494    False\n",
       "3495    False\n",
       "3496    False\n",
       "3497    False\n",
       "3498    False\n",
       "3499    False\n",
       "3500    False\n",
       "3501    False\n",
       "3502    False\n",
       "3503    False\n",
       "3504    False\n",
       "3505    False\n",
       "3506    False\n",
       "Name: positionName, Length: 3507, dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#contains是字符串中的方法。\n",
    "cond = job['positionName'].str.contains('数据分析')\n",
    "cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "hollywood-hobby",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1652, 13)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = job[cond]\n",
    "job.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "communist-estimate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyShortName</th>\n",
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       "      <th>education</th>\n",
       "      <th>financeStage</th>\n",
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       "      <th>salary</th>\n",
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       "      <th>companyLabelList</th>\n",
       "      <th>job_detail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>拉勾网</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>25k-35k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...</td>\n",
       "      <td>[\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]</td>\n",
       "      <td>\\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>OK Group</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>B轮</td>\n",
       "      <td>金融</td>\n",
       "      <td>25k-45k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]</td>\n",
       "      <td>\\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>金山办公软件</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>金山办公软件</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"免费班车\",\"免费体检\",\"地铁周边\"]</td>\n",
       "      <td>[\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]</td>\n",
       "      <td>\\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>鲸鱼外教培优</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"交通补助\",\"绩效奖金\",\"带薪年假\",\"节日礼物\"]</td>\n",
       "      <td>\\n        1.负责业务数据分析工作，基于业务场景，建立核心业务数据模型及预警体系；...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北银消费</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>15k-20k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"绩效奖金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n        职责描述：\\n1、对公司业务数据进行分析，提出有效合理的风控建议；\\n2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>时趣social-touch</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15k-20k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"弹性工作\",\"年终奖金\",\"个性福利\",\"带薪年假\"]</td>\n",
       "      <td>\\n        岗位描述：\\n品牌舆情，社交分析，服务过品牌客户，发布周期性评估报告（美...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>e城e家</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,消费生活</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"绩效奖金\",\"带薪年假\",\"午餐补助\"]</td>\n",
       "      <td>\\n        技能要求：\\n数据分析，SQL，数据挖掘，需求分析，商业数据分析，SPS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>钛氪新媒体科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>8k-16k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        职位职责：\\n1、负责iNews新闻大数据产品数据分析，提出数据问题、分...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>伊澳科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>35k-55k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"股票期权\",\"绩效奖金\",\"年终分红\"]</td>\n",
       "      <td>\\n        职位描述\\n1.基于对业务深入理解的基础上，搭建业务分析模型，为业务提供...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数数科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>A轮</td>\n",
       "      <td>游戏,数据服务</td>\n",
       "      <td>10k-20k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"股票期权\",\"带薪年假\",\"年终分红\"]</td>\n",
       "      <td>\\n        职位描述：\\n\\n1. 负责数据后台的实施落地，引导客户了解产品使用方法...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>省钱快报</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>C轮</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>20k-40k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]</td>\n",
       "      <td>\\n        职位描述：\\n1、负责搜索和推荐业务的数据分析工作；\\n2、基于内外部的...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>省钱快报</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>C轮</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>20k-40k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]</td>\n",
       "      <td>\\n        职位描述： \\n1、负责搜索和推荐业务的数据分析工作 \\n2、基于内外部...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>蛋壳公寓</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>20k-25k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"试用期上公积金\",\"地铁周边\",\"6险1金\"]</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"岗位晋升\",\"年度旅游\"]</td>\n",
       "      <td>\\n岗位职责：\\n1、深入洞察行业、用户、业务背景，分析核心数据，制定科学目标；\\n2、建立...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>钛氪新媒体科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>8k-16k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n\\n\\n\\n职位职责：\\n\\n1、负责iNews新闻大数据产品的数据分析、挖掘、监控、整...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>小帮规划</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>移动互联网,金融</td>\n",
       "      <td>25k-45k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"五险一金\",\"午餐补助\",\"交通补助\",\"带薪年假\"]</td>\n",
       "      <td>\\n        这个职位需要做什么？\\n1. 业务数据分析体系优化：包含但不限于业务数据...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>美图公司</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>硬件</td>\n",
       "      <td>25k-45k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"与大牛共事\",\"福利健全\",\"五险一金\"]</td>\n",
       "      <td>\\n        岗位职责： \\n\\n1、关注市场动态，做行业深度分析，定期产出行业以及竞...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>魔力耳朵</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>8k-15k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"弹性工作\",\"节日礼物\",\"领导好\"]</td>\n",
       "      <td>\\n        【岗位职责】\\n1.基于对教学服务深入理解的基础上，搭建业务分析模型，统...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>用友</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>18k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n数据分析师 岗位职责:1、 负责政府数据产品和项目中的数据治理、数据分析、数据展示工作；...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>跟谁学</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,教育</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>最右</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"连续创业团队\",\"年度旅游\",\"扁平管理\"]</td>\n",
       "      <td>\\n职责:\\n1.对最右产品运营数据进行分析、监测、统计，并形成指标体系；\\n2.制定业务核...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>数据分析实习生</td>\n",
       "      <td>琥珀创想</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>A轮</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>4k-5k</td>\n",
       "      <td>应届毕业生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"提供工作餐\",\"定期团建\",\"交通补助\"]</td>\n",
       "      <td>\\n工作内容： \\n1、负责竞品相关数据监控，定期更新数据，形成相关报表；\\n2、负责对异常...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>数据分析师（教育产品）</td>\n",
       "      <td>跟谁学</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,教育</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>ZMT众盟数据</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>C轮</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>12k-15k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"股票期权\",\"专项奖金\",\"绩效奖金\",\"五险一金\"]</td>\n",
       "      <td>\\n        岗位职责：\\n 1、负责每日部门日常数据报表与分析指标；\\n  2、负责...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>探探</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>移动互联网,社交</td>\n",
       "      <td>25k-45k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"股票期权\",\"带薪年假\",\"岗位晋升\",\"扁平管理\"]</td>\n",
       "      <td>\\n职位职责：\\n1、整体负责探探下产品业务线的各项数据分析相关工作\\n2、协助构建新的数据...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>海捷科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>不限</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>10k-20k</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"定期体检\",\"带薪年假\",\"年度旅游\",\"节日礼物\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、根据业务需求，充分利用现有数据资源，进行数据提取、整理...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>盛业恒泰投资</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>不限</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>8k-15k</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        一 、岗位职责\\n1，搜集行业相关信息，为相关需求者提供更准确的数据信息...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>易观</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>25k-50k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"早十晚七\",\"生日聚会\"]</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]</td>\n",
       "      <td>\\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>易观</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>25k-50k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"早十晚七\",\"生日聚会\"]</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]</td>\n",
       "      <td>\\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>2884</td>\n",
       "      <td>3055</td>\n",
       "      <td>数据分析师（食宿+五险）</td>\n",
       "      <td>潭州教育</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>3k-6k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1.负责部门推广运营及多渠道业务数据的整理和分析，制作数据...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>2885</td>\n",
       "      <td>3056</td>\n",
       "      <td>数据分析实习生(J10694)</td>\n",
       "      <td>树根互联技术有限公司</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>不限</td>\n",
       "      <td>A轮</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>3k-5k</td>\n",
       "      <td>应届毕业生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"帅哥多\",\"领导好\",\"美女多\"]</td>\n",
       "      <td>\\n        工作职责:\\n(1)协助项业务需求和文档梳理工作；\\n(2)协助算法工程...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>2886</td>\n",
       "      <td>3057</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数魔</td>\n",
       "      <td>长沙</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>电商,数据服务</td>\n",
       "      <td>12k-18k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"带薪年假\",\"年度旅游\",\"领导好\"]</td>\n",
       "      <td>\\n        位职责：\\n1、完成因项目需要而开展的数据处理、整合，参与算法模型的开发...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>2887</td>\n",
       "      <td>3058</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>益丰大药房</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商,医疗丨健康</td>\n",
       "      <td>6k-9k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"绩效奖金\",\"五险一金\",\"免费午餐\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、提取商品销售等数据，进行数据分析，制作数据报表及数据可...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>2888</td>\n",
       "      <td>3059</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>南京汇龙</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>5k-7k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n数据岗位要求：（工作地点：长沙 ）\\n工作要求\\n1、电信移动用户维系相关数据统计及分析...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>2889</td>\n",
       "      <td>3060</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>233网校</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>移动互联网,教育</td>\n",
       "      <td>8k-10k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"年底双薪\",\"技能培训\",\"岗位晋升\"]</td>\n",
       "      <td>\\n岗位职责： 1、优化运营数据分析体系，帮助确定各项运营数据指标； 2、配合业务部门给予数...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>2890</td>\n",
       "      <td>3061</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京天意飞扬科技</td>\n",
       "      <td>长沙</td>\n",
       "      <td>少于15人</td>\n",
       "      <td>不限</td>\n",
       "      <td>未融资</td>\n",
       "      <td>电商,移动互联网</td>\n",
       "      <td>8k-12k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n1、能熟练使用SQL分析数据，熟练使用数理统计、数据分析、数据挖掘工具软件（SAS、R、...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>2891</td>\n",
       "      <td>3062</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>蜜獾律师事务所</td>\n",
       "      <td>长沙</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>8k-13k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责： 1.负责业务相关数据的获取、清洗、分析、挖掘和可视化，从数据收集/准备到模型...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>2892</td>\n",
       "      <td>3063</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>蜜獾信息</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>4k-8k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n岗位职责：\\n1． 熟悉公司产品，对公司产品相关的业务数据进行分析、整理，产出基于数据的...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>2893</td>\n",
       "      <td>3064</td>\n",
       "      <td>bi数据分析师</td>\n",
       "      <td>武汉盛博汇</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>大专</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>医疗丨健康,移动互联网</td>\n",
       "      <td>7k-10k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、正确理解业务，完成数据仓库主题设计，核心表设计，各类源...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>2894</td>\n",
       "      <td>3065</td>\n",
       "      <td>数据分析师中级（SC0902）</td>\n",
       "      <td>蜜獾信息</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>6k-10k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n工作职责:1.与中国及美国总部的IT部门，和业务部门紧密协作，搜集专业的房产及交易市场信...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>2895</td>\n",
       "      <td>3066</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>政法频道</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责：\\n负责新闻资讯、用户行为数据等海量数据采集、处理、存储，应用方案的技术选型及...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>3164</td>\n",
       "      <td>3339</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>中地行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务,移动互联网</td>\n",
       "      <td>8k-15k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>[\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]</td>\n",
       "      <td>[\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]</td>\n",
       "      <td>\\n岗位描述：1、负责银行数据分析模型的建立、调优和迭代；2、负责银行数据的清洗加工；3、负...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>3165</td>\n",
       "      <td>3340</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>神彩科技</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>8k-12k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"管吃\",\"领导好\",\"全额五险一金\"]</td>\n",
       "      <td>\\n岗位要求：\\n本科及以上学历，计算机、数学、统计学；\\n1.具有丰富的SQL经验；\\n2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>3166</td>\n",
       "      <td>3341</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>同程旅行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>旅游</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"股票期权\",\"年底双薪\",\"五险一金\"]</td>\n",
       "      <td>\\n工作职责：\\n1、本地生活业务数据分析，订单、营收、用户分级、留存、运营等策略方向分析；...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>3167</td>\n",
       "      <td>3342</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>K2DATA</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"风口行业\",\"顶尖团队\",\"扁平管理\",\"学习型组织\"]</td>\n",
       "      <td>\\n岗位说明：\\n1. 针对重点工业行业（能源，石化，汽车，钢铁、轮胎等）的智能制造与工业互...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>3168</td>\n",
       "      <td>3343</td>\n",
       "      <td>数据分析助理</td>\n",
       "      <td>美能华智能科技</td>\n",
       "      <td>苏州</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>大专</td>\n",
       "      <td>A轮</td>\n",
       "      <td>企业服务,人工智能</td>\n",
       "      <td>4k-6k</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责数据的整理和分析、为人工智能模型提供文字的标注数据...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>3169</td>\n",
       "      <td>3344</td>\n",
       "      <td>数据分析工程师（银行）</td>\n",
       "      <td>中地行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务,移动互联网</td>\n",
       "      <td>12k-24k</td>\n",
       "      <td>不限</td>\n",
       "      <td>[\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]</td>\n",
       "      <td>[\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责银行数据分析模型的建立、调优和迭代；\\n2、负责银...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>3170</td>\n",
       "      <td>3345</td>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>昆山农商银行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>12k-24k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"节日礼物\",\"技能培训\"]</td>\n",
       "      <td>\\n        工作职责:\\n1、根据业务进行数据集市与主维度模型的设计与建设，规范数据...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>3171</td>\n",
       "      <td>3346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>苏州瑞鹏信息技术有限公司</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>12k-24k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        1、日常数据监控，包括日报设计、开发、维护等，完善数据报表体系，及时准确...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>3172</td>\n",
       "      <td>3347</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>同程艺龙</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,旅游</td>\n",
       "      <td>5k-6k</td>\n",
       "      <td>应届毕业生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"最佳雇主\",\"爱旅游\",\"免息贷款\",\"年底双薪\"]</td>\n",
       "      <td>\\n数据分析实习生职位要求：1）计算机、统计学、数学等相关专业，要求实习时间不短于6个月2）...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>3173</td>\n",
       "      <td>3348</td>\n",
       "      <td>专利数据分析师</td>\n",
       "      <td>智慧芽</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]</td>\n",
       "      <td>\\n        工作职责： \\n1. 深度分析专利文本，定出专利文本加工（抽取，分析） ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>3174</td>\n",
       "      <td>3349</td>\n",
       "      <td>游戏数据分析师</td>\n",
       "      <td>友谊时光</td>\n",
       "      <td>苏州</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,游戏</td>\n",
       "      <td>8k-15k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、协助产品监控和跟踪游戏数据，出具敏捷报告；\\n2、对游...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>3175</td>\n",
       "      <td>3350</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>准雀Accunique</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>硕士</td>\n",
       "      <td>未融资</td>\n",
       "      <td>数据服务,其他</td>\n",
       "      <td>10k-20k</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年终分红\",\"绩效奖金\",\"专项奖金\",\"带薪年假\"]</td>\n",
       "      <td>\\n客户需求：科研数据分析、程序设计、疑难专业问题解答等需求（包括但不限于经济、金融、心理、...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>3176</td>\n",
       "      <td>3351</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>瑞小云</td>\n",
       "      <td>苏州</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>12k-18k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"专项奖金\",\"股票期权\",\"年度旅游\"]</td>\n",
       "      <td>\\n职位描述1.用户运营数据分析支撑，通过用户特征、行为数据、产品转化数据等对业务进行挖掘和...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>3177</td>\n",
       "      <td>3352</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>华泛信息</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,其他</td>\n",
       "      <td>10k-15k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"节日礼物\",\"带薪年假\",\"绩效奖金\"]</td>\n",
       "      <td>\\n岗位职责：\\n深入了解项目组的业务需求,在此基础上进行数据收集、数据分析、商业报告的撰写...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>3178</td>\n",
       "      <td>3353</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>德融嘉信</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>6k-12k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责：\\n1. 开展业务专题分析，使用数据挖掘各类算法构建相关的业务模型，完成业务分...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>3404</td>\n",
       "      <td>3580</td>\n",
       "      <td>ETL/大数据/数据分析/实施</td>\n",
       "      <td>格蒂电力</td>\n",
       "      <td>天津</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>未融资</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>6k-12k</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n工作职责\\n1.   负责数据接入、数据整合中的链路配置与调度配置工作。\\n职位要求\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>3405</td>\n",
       "      <td>3581</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>吉城美家</td>\n",
       "      <td>天津</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>7k-14k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"五险一金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n负责站点日常数据分析、提前通过数据分析对业务有预测性、通过数据说话、解决站点管理问题、\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>3406</td>\n",
       "      <td>3582</td>\n",
       "      <td>数据分析支持</td>\n",
       "      <td>云链供应链</td>\n",
       "      <td>天津</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融,企业服务</td>\n",
       "      <td>4k-6k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责部门日常数据报表的制定、维护、优化；\\n2、支持运...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1652 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      level_0  index     positionName companyShortName city companySize  \\\n",
       "0           0      0          高级数据分析师              拉勾网   北京   500-2000人   \n",
       "1           1      1            数据分析师         OK Group   北京   500-2000人   \n",
       "2           2      2          高级数据分析师           金山办公软件   北京     2000人以上   \n",
       "3           3      3            数据分析师           金山办公软件   北京     2000人以上   \n",
       "4           4      4            数据分析师             京东集团   北京     2000人以上   \n",
       "5           5      5            数据分析师           鲸鱼外教培优   北京   500-2000人   \n",
       "6           6      6             数据分析             北银消费   北京    150-500人   \n",
       "7           7      7            数据分析师   时趣social-touch   北京   500-2000人   \n",
       "8           8      8            数据分析师             e城e家   北京     2000人以上   \n",
       "9           9      9           数据分析专员          钛氪新媒体科技   北京     50-150人   \n",
       "10         10     10           数据分析专家             伊澳科技   北京     2000人以上   \n",
       "11         11     11            数据分析师             数数科技   北京      15-50人   \n",
       "12         12     12            数据分析师             省钱快报   北京    150-500人   \n",
       "13         13     13             数据分析             省钱快报   北京    150-500人   \n",
       "14         14     14            数据分析师             蛋壳公寓   北京     2000人以上   \n",
       "15         15     16           数据分析专员          钛氪新媒体科技   北京     50-150人   \n",
       "16         16     17            数据分析师             小帮规划   北京   500-2000人   \n",
       "17         17     18            数据分析师             美图公司   北京     2000人以上   \n",
       "18         18     21            数据分析师             魔力耳朵   北京   500-2000人   \n",
       "19         19     22          高级数据分析师               用友   北京   500-2000人   \n",
       "20         20     23            数据分析师              跟谁学   北京     2000人以上   \n",
       "21         21     24             数据分析               最右   北京   500-2000人   \n",
       "22         22     25          数据分析实习生             琥珀创想   北京     50-150人   \n",
       "23         23     26      数据分析师（教育产品）              跟谁学   北京     2000人以上   \n",
       "24         24     27            数据分析师          ZMT众盟数据   北京   500-2000人   \n",
       "25         25     29          高级数据分析师               探探   北京    150-500人   \n",
       "26         26     30            数据分析师             海捷科技   北京     50-150人   \n",
       "27         27     31            数据分析师           盛业恒泰投资   北京     50-150人   \n",
       "28         28     32            数据分析师               易观   北京    150-500人   \n",
       "29         29     33          高级数据分析师               易观   北京    150-500人   \n",
       "...       ...    ...              ...              ...  ...         ...   \n",
       "1622     2884   3055     数据分析师（食宿+五险）             潭州教育   长沙     2000人以上   \n",
       "1623     2885   3056  数据分析实习生(J10694)       树根互联技术有限公司   长沙    150-500人   \n",
       "1624     2886   3057            数据分析师               数魔   长沙     50-150人   \n",
       "1625     2887   3058           数据分析主管            益丰大药房   长沙     2000人以上   \n",
       "1626     2888   3059             数据分析             南京汇龙   长沙    150-500人   \n",
       "1627     2889   3060            数据分析师            233网校   长沙    150-500人   \n",
       "1628     2890   3061             数据分析         北京天意飞扬科技   长沙       少于15人   \n",
       "1629     2891   3062             数据分析          蜜獾律师事务所   长沙     50-150人   \n",
       "1630     2892   3063            数据分析师             蜜獾信息   长沙    150-500人   \n",
       "1631     2893   3064          bi数据分析师            武汉盛博汇   长沙    150-500人   \n",
       "1632     2894   3065  数据分析师中级（SC0902）             蜜獾信息   长沙    150-500人   \n",
       "1633     2895   3066           大数据分析师             政法频道   长沙    150-500人   \n",
       "1634     3164   3339          数据分析工程师              中地行   苏州     50-150人   \n",
       "1635     3165   3340          数据分析工程师             神彩科技   苏州    150-500人   \n",
       "1636     3166   3341            数据分析师             同程旅行   苏州     2000人以上   \n",
       "1637     3167   3342          高级数据分析师           K2DATA   苏州    150-500人   \n",
       "1638     3168   3343           数据分析助理          美能华智能科技   苏州      15-50人   \n",
       "1639     3169   3344      数据分析工程师（银行）              中地行   苏州     50-150人   \n",
       "1640     3170   3345            数据分析岗           昆山农商银行   苏州   500-2000人   \n",
       "1641     3171   3346            数据分析师     苏州瑞鹏信息技术有限公司   苏州     50-150人   \n",
       "1642     3172   3347            数据分析师             同程艺龙   苏州     2000人以上   \n",
       "1643     3173   3348          专利数据分析师              智慧芽   苏州    150-500人   \n",
       "1644     3174   3349          游戏数据分析师             友谊时光   苏州   500-2000人   \n",
       "1645     3175   3350            数据分析师      准雀Accunique   苏州     2000人以上   \n",
       "1646     3176   3351            数据分析师              瑞小云   苏州      15-50人   \n",
       "1647     3177   3352            数据分析师             华泛信息   苏州     2000人以上   \n",
       "1648     3178   3353           大数据分析师             德融嘉信   苏州     50-150人   \n",
       "1649     3404   3580  ETL/大数据/数据分析/实施             格蒂电力   天津   500-2000人   \n",
       "1650     3405   3581            数据分析师             吉城美家   天津     2000人以上   \n",
       "1651     3406   3582           数据分析支持            云链供应链   天津      15-50人   \n",
       "\n",
       "     education financeStage industryField   salary workYear  \\\n",
       "0           本科        D轮及以上          企业服务  25k-35k    5-10年   \n",
       "1           大专           B轮            金融  25k-45k    5-10年   \n",
       "2           本科         上市公司         移动互联网  15k-25k     3-5年   \n",
       "3           本科         上市公司         移动互联网  15k-25k     1-3年   \n",
       "4           本科         上市公司            电商  15k-30k     3-5年   \n",
       "5           本科           B轮            教育  15k-30k     3-5年   \n",
       "6           本科        不需要融资            金融  15k-20k     1-3年   \n",
       "7           本科        D轮及以上         移动互联网  15k-20k     3-5年   \n",
       "8           本科        不需要融资    移动互联网,消费生活  15k-25k     3-5年   \n",
       "9           本科          未融资         移动互联网   8k-16k     1-3年   \n",
       "10          本科         上市公司      移动互联网,电商  35k-55k    5-10年   \n",
       "11          本科           A轮       游戏,数据服务  10k-20k     1-3年   \n",
       "12          本科           C轮      移动互联网,电商  20k-40k     3-5年   \n",
       "13          本科           C轮      移动互联网,电商  20k-40k     3-5年   \n",
       "14          本科         上市公司          消费生活  20k-25k     3-5年   \n",
       "15          本科          未融资         移动互联网   8k-16k     1-3年   \n",
       "16          本科           B轮      移动互联网,金融  25k-45k     3-5年   \n",
       "17          本科         上市公司            硬件  25k-45k     3-5年   \n",
       "18          本科           B轮            教育   8k-15k     1-3年   \n",
       "19          本科         上市公司    移动互联网,数据服务  18k-30k     3-5年   \n",
       "20          本科         上市公司      移动互联网,教育  15k-25k     3-5年   \n",
       "21          本科        D轮及以上         文娱丨内容  15k-30k     1-3年   \n",
       "22          本科           A轮         移动互联网    4k-5k    应届毕业生   \n",
       "23          本科         上市公司      移动互联网,教育  15k-30k     3-5年   \n",
       "24          大专           C轮    移动互联网,数据服务  12k-15k     1-3年   \n",
       "25          本科        D轮及以上      移动互联网,社交  25k-45k     3-5年   \n",
       "26          不限        不需要融资    移动互联网,数据服务  10k-20k       不限   \n",
       "27          不限          未融资            金融   8k-15k       不限   \n",
       "28          本科           B轮    移动互联网,数据服务  25k-50k     3-5年   \n",
       "29          本科           B轮    移动互联网,数据服务  25k-50k    5-10年   \n",
       "...        ...          ...           ...      ...      ...   \n",
       "1622        大专           B轮            教育    3k-6k     1-3年   \n",
       "1623        不限           A轮    移动互联网,企业服务    3k-5k    应届毕业生   \n",
       "1624        本科          天使轮       电商,数据服务  12k-18k     3-5年   \n",
       "1625        大专         上市公司      电商,医疗丨健康    6k-9k     3-5年   \n",
       "1626        本科          未融资         移动互联网    5k-7k     1-3年   \n",
       "1627        本科          天使轮      移动互联网,教育   8k-10k     3-5年   \n",
       "1628        不限          未融资      电商,移动互联网   8k-12k     1-3年   \n",
       "1629        本科          未融资            其他   8k-13k     3-5年   \n",
       "1630        本科        不需要融资         移动互联网    4k-8k     1-3年   \n",
       "1631        大专        不需要融资   医疗丨健康,移动互联网   7k-10k     1-3年   \n",
       "1632        本科        不需要融资         移动互联网   6k-10k     1-3年   \n",
       "1633        硕士        不需要融资            其他  15k-30k    5-10年   \n",
       "1634        本科        不需要融资    数据服务,移动互联网   8k-15k     1-3年   \n",
       "1635        本科        不需要融资          数据服务   8k-12k     1-3年   \n",
       "1636        本科         上市公司            旅游  15k-30k     3-5年   \n",
       "1637        本科           B轮          数据服务  15k-30k     3-5年   \n",
       "1638        大专           A轮     企业服务,人工智能    4k-6k       不限   \n",
       "1639        本科        不需要融资    数据服务,移动互联网  12k-24k       不限   \n",
       "1640        本科          未融资            金融  12k-24k     1-3年   \n",
       "1641        硕士        不需要融资    移动互联网,数据服务  12k-24k     1-3年   \n",
       "1642        本科         上市公司      移动互联网,旅游    5k-6k    应届毕业生   \n",
       "1643        本科        D轮及以上         移动互联网  15k-25k       不限   \n",
       "1644        本科         上市公司      移动互联网,游戏   8k-15k     1-3年   \n",
       "1645        硕士          未融资       数据服务,其他  10k-20k       不限   \n",
       "1646        本科        不需要融资         移动互联网  12k-18k     3-5年   \n",
       "1647        大专        不需要融资      移动互联网,其他  10k-15k     1-3年   \n",
       "1648        本科        不需要融资         移动互联网   6k-12k     1-3年   \n",
       "1649        大专          未融资          企业服务   6k-12k     3-5年   \n",
       "1650        本科          未融资         移动互联网   7k-14k     1-3年   \n",
       "1651        本科          未融资       金融,企业服务    4k-6k     1-3年   \n",
       "\n",
       "                                                 hitags  \\\n",
       "0     [\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...   \n",
       "1                                                   NaN   \n",
       "2                                                   NaN   \n",
       "3                                                   NaN   \n",
       "4                                [\"免费班车\",\"免费体检\",\"地铁周边\"]   \n",
       "5                                                   NaN   \n",
       "6                                                   NaN   \n",
       "7                                                   NaN   \n",
       "8                                                   NaN   \n",
       "9                                                   NaN   \n",
       "10                                                  NaN   \n",
       "11                                                  NaN   \n",
       "12                                                  NaN   \n",
       "13                                                  NaN   \n",
       "14                            [\"试用期上公积金\",\"地铁周边\",\"6险1金\"]   \n",
       "15                                                  NaN   \n",
       "16                                                  NaN   \n",
       "17                                                  NaN   \n",
       "18                                                  NaN   \n",
       "19                                                  NaN   \n",
       "20                                                  NaN   \n",
       "21                                                  NaN   \n",
       "22                                                  NaN   \n",
       "23                                                  NaN   \n",
       "24                                                  NaN   \n",
       "25                                                  NaN   \n",
       "26                                                  NaN   \n",
       "27                                                  NaN   \n",
       "28                                      [\"早十晚七\",\"生日聚会\"]   \n",
       "29                                      [\"早十晚七\",\"生日聚会\"]   \n",
       "...                                                 ...   \n",
       "1622                                                NaN   \n",
       "1623                                                NaN   \n",
       "1624                                                NaN   \n",
       "1625                                                NaN   \n",
       "1626                                                NaN   \n",
       "1627                                                NaN   \n",
       "1628                                                NaN   \n",
       "1629                                                NaN   \n",
       "1630                                                NaN   \n",
       "1631                                                NaN   \n",
       "1632                                                NaN   \n",
       "1633                                                NaN   \n",
       "1634                     [\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]   \n",
       "1635                                                NaN   \n",
       "1636                                                NaN   \n",
       "1637                                                NaN   \n",
       "1638                                                NaN   \n",
       "1639                     [\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]   \n",
       "1640                                                NaN   \n",
       "1641                                                NaN   \n",
       "1642                                                NaN   \n",
       "1643                                                NaN   \n",
       "1644                                                NaN   \n",
       "1645                                                NaN   \n",
       "1646                                                NaN   \n",
       "1647                                                NaN   \n",
       "1648                                                NaN   \n",
       "1649                                                NaN   \n",
       "1650                                                NaN   \n",
       "1651                                                NaN   \n",
       "\n",
       "                     companyLabelList  \\\n",
       "0       [\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]   \n",
       "1        [\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]   \n",
       "2       [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "3       [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "4       [\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]   \n",
       "5       [\"交通补助\",\"绩效奖金\",\"带薪年假\",\"节日礼物\"]   \n",
       "6       [\"节日礼物\",\"技能培训\",\"绩效奖金\",\"岗位晋升\"]   \n",
       "7       [\"弹性工作\",\"年终奖金\",\"个性福利\",\"带薪年假\"]   \n",
       "8       [\"年底双薪\",\"绩效奖金\",\"带薪年假\",\"午餐补助\"]   \n",
       "9                                  []   \n",
       "10      [\"专项奖金\",\"股票期权\",\"绩效奖金\",\"年终分红\"]   \n",
       "11      [\"节日礼物\",\"股票期权\",\"带薪年假\",\"年终分红\"]   \n",
       "12      [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]   \n",
       "13      [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]   \n",
       "14      [\"技能培训\",\"带薪年假\",\"岗位晋升\",\"年度旅游\"]   \n",
       "15                                 []   \n",
       "16      [\"五险一金\",\"午餐补助\",\"交通补助\",\"带薪年假\"]   \n",
       "17     [\"节日礼物\",\"与大牛共事\",\"福利健全\",\"五险一金\"]   \n",
       "18       [\"带薪年假\",\"弹性工作\",\"节日礼物\",\"领导好\"]   \n",
       "19                                 []   \n",
       "20      [\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]   \n",
       "21    [\"技能培训\",\"连续创业团队\",\"年度旅游\",\"扁平管理\"]   \n",
       "22     [\"绩效奖金\",\"提供工作餐\",\"定期团建\",\"交通补助\"]   \n",
       "23      [\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]   \n",
       "24      [\"股票期权\",\"专项奖金\",\"绩效奖金\",\"五险一金\"]   \n",
       "25      [\"股票期权\",\"带薪年假\",\"岗位晋升\",\"扁平管理\"]   \n",
       "26      [\"定期体检\",\"带薪年假\",\"年度旅游\",\"节日礼物\"]   \n",
       "27                                 []   \n",
       "28      [\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]   \n",
       "29      [\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]   \n",
       "...                               ...   \n",
       "1622                               []   \n",
       "1623       [\"专项奖金\",\"帅哥多\",\"领导好\",\"美女多\"]   \n",
       "1624     [\"年底双薪\",\"带薪年假\",\"年度旅游\",\"领导好\"]   \n",
       "1625    [\"年底双薪\",\"绩效奖金\",\"五险一金\",\"免费午餐\"]   \n",
       "1626                               []   \n",
       "1627    [\"节日礼物\",\"年底双薪\",\"技能培训\",\"岗位晋升\"]   \n",
       "1628                               []   \n",
       "1629                               []   \n",
       "1630    [\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1631                               []   \n",
       "1632    [\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1633                               []   \n",
       "1634    [\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]   \n",
       "1635     [\"带薪年假\",\"管吃\",\"领导好\",\"全额五险一金\"]   \n",
       "1636    [\"绩效奖金\",\"股票期权\",\"年底双薪\",\"五险一金\"]   \n",
       "1637   [\"风口行业\",\"顶尖团队\",\"扁平管理\",\"学习型组织\"]   \n",
       "1638                               []   \n",
       "1639    [\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]   \n",
       "1640    [\"专项奖金\",\"午餐补助\",\"节日礼物\",\"技能培训\"]   \n",
       "1641                               []   \n",
       "1642     [\"最佳雇主\",\"爱旅游\",\"免息贷款\",\"年底双薪\"]   \n",
       "1643    [\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]   \n",
       "1644    [\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]   \n",
       "1645    [\"年终分红\",\"绩效奖金\",\"专项奖金\",\"带薪年假\"]   \n",
       "1646    [\"绩效奖金\",\"专项奖金\",\"股票期权\",\"年度旅游\"]   \n",
       "1647    [\"技能培训\",\"节日礼物\",\"带薪年假\",\"绩效奖金\"]   \n",
       "1648                               []   \n",
       "1649    [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"岗位晋升\"]   \n",
       "1650                  [\"五险一金\",\"岗位晋升\"]   \n",
       "1651                               []   \n",
       "\n",
       "                                             job_detail  \n",
       "0     \\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...  \n",
       "1     \\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...  \n",
       "2     \\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...  \n",
       "3     \\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...  \n",
       "4     \\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...  \n",
       "5     \\n        1.负责业务数据分析工作，基于业务场景，建立核心业务数据模型及预警体系；...  \n",
       "6     \\n        职责描述：\\n1、对公司业务数据进行分析，提出有效合理的风控建议；\\n2...  \n",
       "7     \\n        岗位描述：\\n品牌舆情，社交分析，服务过品牌客户，发布周期性评估报告（美...  \n",
       "8     \\n        技能要求：\\n数据分析，SQL，数据挖掘，需求分析，商业数据分析，SPS...  \n",
       "9     \\n        职位职责：\\n1、负责iNews新闻大数据产品数据分析，提出数据问题、分...  \n",
       "10    \\n        职位描述\\n1.基于对业务深入理解的基础上，搭建业务分析模型，为业务提供...  \n",
       "11    \\n        职位描述：\\n\\n1. 负责数据后台的实施落地，引导客户了解产品使用方法...  \n",
       "12    \\n        职位描述：\\n1、负责搜索和推荐业务的数据分析工作；\\n2、基于内外部的...  \n",
       "13    \\n        职位描述： \\n1、负责搜索和推荐业务的数据分析工作 \\n2、基于内外部...  \n",
       "14    \\n岗位职责：\\n1、深入洞察行业、用户、业务背景，分析核心数据，制定科学目标；\\n2、建立...  \n",
       "15    \\n\\n\\n\\n职位职责：\\n\\n1、负责iNews新闻大数据产品的数据分析、挖掘、监控、整...  \n",
       "16    \\n        这个职位需要做什么？\\n1. 业务数据分析体系优化：包含但不限于业务数据...  \n",
       "17    \\n        岗位职责： \\n\\n1、关注市场动态，做行业深度分析，定期产出行业以及竞...  \n",
       "18    \\n        【岗位职责】\\n1.基于对教学服务深入理解的基础上，搭建业务分析模型，统...  \n",
       "19    \\n数据分析师 岗位职责:1、 负责政府数据产品和项目中的数据治理、数据分析、数据展示工作；...  \n",
       "20    \\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...  \n",
       "21    \\n职责:\\n1.对最右产品运营数据进行分析、监测、统计，并形成指标体系；\\n2.制定业务核...  \n",
       "22    \\n工作内容： \\n1、负责竞品相关数据监控，定期更新数据，形成相关报表；\\n2、负责对异常...  \n",
       "23    \\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...  \n",
       "24    \\n        岗位职责：\\n 1、负责每日部门日常数据报表与分析指标；\\n  2、负责...  \n",
       "25    \\n职位职责：\\n1、整体负责探探下产品业务线的各项数据分析相关工作\\n2、协助构建新的数据...  \n",
       "26    \\n        岗位职责：\\n1、根据业务需求，充分利用现有数据资源，进行数据提取、整理...  \n",
       "27    \\n        一 、岗位职责\\n1，搜集行业相关信息，为相关需求者提供更准确的数据信息...  \n",
       "28    \\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...  \n",
       "29    \\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...  \n",
       "...                                                 ...  \n",
       "1622  \\n        岗位职责：\\n1.负责部门推广运营及多渠道业务数据的整理和分析，制作数据...  \n",
       "1623  \\n        工作职责:\\n(1)协助项业务需求和文档梳理工作；\\n(2)协助算法工程...  \n",
       "1624  \\n        位职责：\\n1、完成因项目需要而开展的数据处理、整合，参与算法模型的开发...  \n",
       "1625  \\n        岗位职责：\\n1、提取商品销售等数据，进行数据分析，制作数据报表及数据可...  \n",
       "1626  \\n数据岗位要求：（工作地点：长沙 ）\\n工作要求\\n1、电信移动用户维系相关数据统计及分析...  \n",
       "1627  \\n岗位职责： 1、优化运营数据分析体系，帮助确定各项运营数据指标； 2、配合业务部门给予数...  \n",
       "1628  \\n1、能熟练使用SQL分析数据，熟练使用数理统计、数据分析、数据挖掘工具软件（SAS、R、...  \n",
       "1629  \\n岗位职责： 1.负责业务相关数据的获取、清洗、分析、挖掘和可视化，从数据收集/准备到模型...  \n",
       "1630  \\n岗位职责：\\n1． 熟悉公司产品，对公司产品相关的业务数据进行分析、整理，产出基于数据的...  \n",
       "1631  \\n        岗位职责：\\n1、正确理解业务，完成数据仓库主题设计，核心表设计，各类源...  \n",
       "1632  \\n工作职责:1.与中国及美国总部的IT部门，和业务部门紧密协作，搜集专业的房产及交易市场信...  \n",
       "1633  \\n岗位职责：\\n负责新闻资讯、用户行为数据等海量数据采集、处理、存储，应用方案的技术选型及...  \n",
       "1634  \\n岗位描述：1、负责银行数据分析模型的建立、调优和迭代；2、负责银行数据的清洗加工；3、负...  \n",
       "1635  \\n岗位要求：\\n本科及以上学历，计算机、数学、统计学；\\n1.具有丰富的SQL经验；\\n2...  \n",
       "1636  \\n工作职责：\\n1、本地生活业务数据分析，订单、营收、用户分级、留存、运营等策略方向分析；...  \n",
       "1637  \\n岗位说明：\\n1. 针对重点工业行业（能源，石化，汽车，钢铁、轮胎等）的智能制造与工业互...  \n",
       "1638  \\n        岗位职责：\\n1、负责数据的整理和分析、为人工智能模型提供文字的标注数据...  \n",
       "1639  \\n        岗位职责：\\n1、负责银行数据分析模型的建立、调优和迭代；\\n2、负责银...  \n",
       "1640  \\n        工作职责:\\n1、根据业务进行数据集市与主维度模型的设计与建设，规范数据...  \n",
       "1641  \\n        1、日常数据监控，包括日报设计、开发、维护等，完善数据报表体系，及时准确...  \n",
       "1642  \\n数据分析实习生职位要求：1）计算机、统计学、数学等相关专业，要求实习时间不短于6个月2）...  \n",
       "1643  \\n        工作职责： \\n1. 深度分析专利文本，定出专利文本加工（抽取，分析） ...  \n",
       "1644  \\n        岗位职责：\\n1、协助产品监控和跟踪游戏数据，出具敏捷报告；\\n2、对游...  \n",
       "1645  \\n客户需求：科研数据分析、程序设计、疑难专业问题解答等需求（包括但不限于经济、金融、心理、...  \n",
       "1646  \\n职位描述1.用户运营数据分析支撑，通过用户特征、行为数据、产品转化数据等对业务进行挖掘和...  \n",
       "1647  \\n岗位职责：\\n深入了解项目组的业务需求,在此基础上进行数据收集、数据分析、商业报告的撰写...  \n",
       "1648  \\n岗位职责：\\n1. 开展业务专题分析，使用数据挖掘各类算法构建相关的业务模型，完成业务分...  \n",
       "1649  \\n工作职责\\n1.   负责数据接入、数据整合中的链路配置与调度配置工作。\\n职位要求\\n...  \n",
       "1650    \\n负责站点日常数据分析、提前通过数据分析对业务有预测性、通过数据说话、解决站点管理问题、\\n  \n",
       "1651  \\n        岗位职责：\\n1、负责部门日常数据报表的制定、维护、优化；\\n2、支持运...  \n",
       "\n",
       "[1652 rows x 14 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#再重新设置一下行索引\n",
    "job.reset_index(inplace=True)\n",
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaning-playback",
   "metadata": {},
   "source": [
    "### 薪水"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "thorough-break",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       30.0\n",
       "1       35.0\n",
       "2       20.0\n",
       "3       20.0\n",
       "4       22.5\n",
       "5       22.5\n",
       "6       17.5\n",
       "7       17.5\n",
       "8       20.0\n",
       "9       12.0\n",
       "10      45.0\n",
       "11      15.0\n",
       "12      30.0\n",
       "13      30.0\n",
       "14      22.5\n",
       "15      12.0\n",
       "16      35.0\n",
       "17      35.0\n",
       "18      11.5\n",
       "19      24.0\n",
       "20      20.0\n",
       "21      22.5\n",
       "22       4.5\n",
       "23      22.5\n",
       "24      13.5\n",
       "25      35.0\n",
       "26      15.0\n",
       "27      11.5\n",
       "28      37.5\n",
       "29      37.5\n",
       "        ... \n",
       "1622     4.5\n",
       "1623     4.0\n",
       "1624    15.0\n",
       "1625     7.5\n",
       "1626     6.0\n",
       "1627     9.0\n",
       "1628    10.0\n",
       "1629    10.5\n",
       "1630     6.0\n",
       "1631     8.5\n",
       "1632     8.0\n",
       "1633    22.5\n",
       "1634    11.5\n",
       "1635    10.0\n",
       "1636    22.5\n",
       "1637    22.5\n",
       "1638     5.0\n",
       "1639    18.0\n",
       "1640    18.0\n",
       "1641    18.0\n",
       "1642     5.5\n",
       "1643    20.0\n",
       "1644    11.5\n",
       "1645    15.0\n",
       "1646    15.0\n",
       "1647    12.5\n",
       "1648     9.0\n",
       "1649     9.0\n",
       "1650    10.5\n",
       "1651     5.0\n",
       "Name: salary, Length: 1652, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1将k都转为小写---------2提取出**k-**k的两个薪资数值,------------3求两个薪资的平均值\n",
    "# 注意applymap的用法！！此处用apply和map都不行！！\n",
    "#map操作serious，applymap操作dataframe\n",
    "job['salary'] = job['salary'].str.lower().str.extract(r'(\\d+)[k]-(\\d+)[k]').\\\n",
    "              applymap(lambda x:int(x)).mean(axis=1)\n",
    "job['salary']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "administrative-hands",
   "metadata": {},
   "source": [
    "### 技能要求"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lyric-client",
   "metadata": {},
   "source": [
    "Python  \n",
    "SQL  \n",
    "Tableau  \n",
    "Excel  \n",
    "SPSS/SAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "viral-warrior",
   "metadata": {},
   "outputs": [],
   "source": [
    "job['job_detail'] = job['job_detail'].str.lower()#变成小写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "super-nigeria",
   "metadata": {},
   "outputs": [],
   "source": [
    "job['Python'] = job['job_detail'].map(lambda x: 1 if 'python' in  x else 0)\n",
    "job['SQL'] = job['job_detail'].map(lambda x: 1 if 'sql' in  x else 0)\n",
    "job['Tableau'] = job['job_detail'].map(lambda x: 1 if 'tableau' in  x else 0)\n",
    "job['Excel'] = job['job_detail'].map(lambda x: 1 if 'excel' in  x else 0)\n",
    "job['SPSS/SAS'] = job['job_detail'].map(lambda x: 1 if ('spss' in  x) or('sas' in  x) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "engaging-foundation",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
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       "      <th>positionName</th>\n",
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       "      <th>salary</th>\n",
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       "      <th>hitags</th>\n",
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       "      <th>SPSS/SAS</th>\n",
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       "      <td>移动互联网</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...</td>\n",
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       "      <td>数据分析师</td>\n",
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       "      <td>移动互联网</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...</td>\n",
       "      <td>0</td>\n",
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       "      <td>[\"免费班车\",\"免费体检\",\"地铁周边\"]</td>\n",
       "      <td>[\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]</td>\n",
       "      <td>\\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...</td>\n",
       "      <td>0</td>\n",
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       "      <td>数据分析师</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>[\"交通补助\",\"绩效奖金\",\"带薪年假\",\"节日礼物\"]</td>\n",
       "      <td>\\n        1.负责业务数据分析工作，基于业务场景，建立核心业务数据模型及预警体系；...</td>\n",
       "      <td>0</td>\n",
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       "      <td>数据分析</td>\n",
       "      <td>北银消费</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>17.5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"绩效奖金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n        职责描述：\\n1、对公司业务数据进行分析，提出有效合理的风控建议；\\n2...</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>时趣social-touch</td>\n",
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       "      <td>本科</td>\n",
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       "      <td>移动互联网</td>\n",
       "      <td>17.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"弹性工作\",\"年终奖金\",\"个性福利\",\"带薪年假\"]</td>\n",
       "      <td>\\n        岗位描述：\\n品牌舆情，社交分析，服务过品牌客户，发布周期性评估报告（美...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>8</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>e城e家</td>\n",
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       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,消费生活</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"绩效奖金\",\"带薪年假\",\"午餐补助\"]</td>\n",
       "      <td>\\n        技能要求：\\n数据分析，sql，数据挖掘，需求分析，商业数据分析，sps...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>9</td>\n",
       "      <td>9</td>\n",
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       "      <td>钛氪新媒体科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        职位职责：\\n1、负责inews新闻大数据产品数据分析，提出数据问题、分...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>伊澳科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"股票期权\",\"绩效奖金\",\"年终分红\"]</td>\n",
       "      <td>\\n        职位描述\\n1.基于对业务深入理解的基础上，搭建业务分析模型，为业务提供...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数数科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>A轮</td>\n",
       "      <td>游戏,数据服务</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"股票期权\",\"带薪年假\",\"年终分红\"]</td>\n",
       "      <td>\\n        职位描述：\\n\\n1. 负责数据后台的实施落地，引导客户了解产品使用方法...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>省钱快报</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>C轮</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]</td>\n",
       "      <td>\\n        职位描述：\\n1、负责搜索和推荐业务的数据分析工作；\\n2、基于内外部的...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>省钱快报</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>C轮</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]</td>\n",
       "      <td>\\n        职位描述： \\n1、负责搜索和推荐业务的数据分析工作 \\n2、基于内外部...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>蛋壳公寓</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"试用期上公积金\",\"地铁周边\",\"6险1金\"]</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"岗位晋升\",\"年度旅游\"]</td>\n",
       "      <td>\\n岗位职责：\\n1、深入洞察行业、用户、业务背景，分析核心数据，制定科学目标；\\n2、建立...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>钛氪新媒体科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n\\n\\n\\n职位职责：\\n\\n1、负责inews新闻大数据产品的数据分析、挖掘、监控、整...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>小帮规划</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>移动互联网,金融</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"五险一金\",\"午餐补助\",\"交通补助\",\"带薪年假\"]</td>\n",
       "      <td>\\n        这个职位需要做什么？\\n1. 业务数据分析体系优化：包含但不限于业务数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>美图公司</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>硬件</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"与大牛共事\",\"福利健全\",\"五险一金\"]</td>\n",
       "      <td>\\n        岗位职责： \\n\\n1、关注市场动态，做行业深度分析，定期产出行业以及竞...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>魔力耳朵</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"弹性工作\",\"节日礼物\",\"领导好\"]</td>\n",
       "      <td>\\n        【岗位职责】\\n1.基于对教学服务深入理解的基础上，搭建业务分析模型，统...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>用友</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n数据分析师 岗位职责:1、 负责政府数据产品和项目中的数据治理、数据分析、数据展示工作；...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>跟谁学</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,教育</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>最右</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"连续创业团队\",\"年度旅游\",\"扁平管理\"]</td>\n",
       "      <td>\\n职责:\\n1.对最右产品运营数据进行分析、监测、统计，并形成指标体系；\\n2.制定业务核...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>数据分析实习生</td>\n",
       "      <td>琥珀创想</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>A轮</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>4.5</td>\n",
       "      <td>应届毕业生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"提供工作餐\",\"定期团建\",\"交通补助\"]</td>\n",
       "      <td>\\n工作内容： \\n1、负责竞品相关数据监控，定期更新数据，形成相关报表；\\n2、负责对异常...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>数据分析师（教育产品）</td>\n",
       "      <td>跟谁学</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,教育</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>ZMT众盟数据</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>C轮</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"股票期权\",\"专项奖金\",\"绩效奖金\",\"五险一金\"]</td>\n",
       "      <td>\\n        岗位职责：\\n 1、负责每日部门日常数据报表与分析指标；\\n  2、负责...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>探探</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>移动互联网,社交</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"股票期权\",\"带薪年假\",\"岗位晋升\",\"扁平管理\"]</td>\n",
       "      <td>\\n职位职责：\\n1、整体负责探探下产品业务线的各项数据分析相关工作\\n2、协助构建新的数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>海捷科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>不限</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>15.0</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"定期体检\",\"带薪年假\",\"年度旅游\",\"节日礼物\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、根据业务需求，充分利用现有数据资源，进行数据提取、整理...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>盛业恒泰投资</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>不限</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>11.5</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        一 、岗位职责\\n1，搜集行业相关信息，为相关需求者提供更准确的数据信息...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>易观</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>37.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"早十晚七\",\"生日聚会\"]</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]</td>\n",
       "      <td>\\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>易观</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>37.5</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"早十晚七\",\"生日聚会\"]</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]</td>\n",
       "      <td>\\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>2884</td>\n",
       "      <td>3055</td>\n",
       "      <td>数据分析师（食宿+五险）</td>\n",
       "      <td>潭州教育</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1.负责部门推广运营及多渠道业务数据的整理和分析，制作数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>2885</td>\n",
       "      <td>3056</td>\n",
       "      <td>数据分析实习生(J10694)</td>\n",
       "      <td>树根互联技术有限公司</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>不限</td>\n",
       "      <td>A轮</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>4.0</td>\n",
       "      <td>应届毕业生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"帅哥多\",\"领导好\",\"美女多\"]</td>\n",
       "      <td>\\n        工作职责:\\n(1)协助项业务需求和文档梳理工作；\\n(2)协助算法工程...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>2886</td>\n",
       "      <td>3057</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数魔</td>\n",
       "      <td>长沙</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>电商,数据服务</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"带薪年假\",\"年度旅游\",\"领导好\"]</td>\n",
       "      <td>\\n        位职责：\\n1、完成因项目需要而开展的数据处理、整合，参与算法模型的开发...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>2887</td>\n",
       "      <td>3058</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>益丰大药房</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商,医疗丨健康</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"绩效奖金\",\"五险一金\",\"免费午餐\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、提取商品销售等数据，进行数据分析，制作数据报表及数据可...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>2888</td>\n",
       "      <td>3059</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>南京汇龙</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n数据岗位要求：（工作地点：长沙 ）\\n工作要求\\n1、电信移动用户维系相关数据统计及分析...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>2889</td>\n",
       "      <td>3060</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>233网校</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>移动互联网,教育</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"年底双薪\",\"技能培训\",\"岗位晋升\"]</td>\n",
       "      <td>\\n岗位职责： 1、优化运营数据分析体系，帮助确定各项运营数据指标； 2、配合业务部门给予数...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>2890</td>\n",
       "      <td>3061</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京天意飞扬科技</td>\n",
       "      <td>长沙</td>\n",
       "      <td>少于15人</td>\n",
       "      <td>不限</td>\n",
       "      <td>未融资</td>\n",
       "      <td>电商,移动互联网</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n1、能熟练使用sql分析数据，熟练使用数理统计、数据分析、数据挖掘工具软件（sas、r、...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>2891</td>\n",
       "      <td>3062</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>蜜獾律师事务所</td>\n",
       "      <td>长沙</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责： 1.负责业务相关数据的获取、清洗、分析、挖掘和可视化，从数据收集/准备到模型...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>2892</td>\n",
       "      <td>3063</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>蜜獾信息</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n岗位职责：\\n1． 熟悉公司产品，对公司产品相关的业务数据进行分析、整理，产出基于数据的...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>2893</td>\n",
       "      <td>3064</td>\n",
       "      <td>bi数据分析师</td>\n",
       "      <td>武汉盛博汇</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>大专</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>医疗丨健康,移动互联网</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、正确理解业务，完成数据仓库主题设计，核心表设计，各类源...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>2894</td>\n",
       "      <td>3065</td>\n",
       "      <td>数据分析师中级（SC0902）</td>\n",
       "      <td>蜜獾信息</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n工作职责:1.与中国及美国总部的it部门，和业务部门紧密协作，搜集专业的房产及交易市场信...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>2895</td>\n",
       "      <td>3066</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>政法频道</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>22.5</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责：\\n负责新闻资讯、用户行为数据等海量数据采集、处理、存储，应用方案的技术选型及...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>3164</td>\n",
       "      <td>3339</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>中地行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务,移动互联网</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>[\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]</td>\n",
       "      <td>[\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]</td>\n",
       "      <td>\\n岗位描述：1、负责银行数据分析模型的建立、调优和迭代；2、负责银行数据的清洗加工；3、负...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>3165</td>\n",
       "      <td>3340</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>神彩科技</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"管吃\",\"领导好\",\"全额五险一金\"]</td>\n",
       "      <td>\\n岗位要求：\\n本科及以上学历，计算机、数学、统计学；\\n1.具有丰富的sql经验；\\n2...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>3166</td>\n",
       "      <td>3341</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>同程旅行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>旅游</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"股票期权\",\"年底双薪\",\"五险一金\"]</td>\n",
       "      <td>\\n工作职责：\\n1、本地生活业务数据分析，订单、营收、用户分级、留存、运营等策略方向分析；...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>3167</td>\n",
       "      <td>3342</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>K2DATA</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"风口行业\",\"顶尖团队\",\"扁平管理\",\"学习型组织\"]</td>\n",
       "      <td>\\n岗位说明：\\n1. 针对重点工业行业（能源，石化，汽车，钢铁、轮胎等）的智能制造与工业互...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>3168</td>\n",
       "      <td>3343</td>\n",
       "      <td>数据分析助理</td>\n",
       "      <td>美能华智能科技</td>\n",
       "      <td>苏州</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>大专</td>\n",
       "      <td>A轮</td>\n",
       "      <td>企业服务,人工智能</td>\n",
       "      <td>5.0</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责数据的整理和分析、为人工智能模型提供文字的标注数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>3169</td>\n",
       "      <td>3344</td>\n",
       "      <td>数据分析工程师（银行）</td>\n",
       "      <td>中地行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务,移动互联网</td>\n",
       "      <td>18.0</td>\n",
       "      <td>不限</td>\n",
       "      <td>[\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]</td>\n",
       "      <td>[\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责银行数据分析模型的建立、调优和迭代；\\n2、负责银...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>3170</td>\n",
       "      <td>3345</td>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>昆山农商银行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"节日礼物\",\"技能培训\"]</td>\n",
       "      <td>\\n        工作职责:\\n1、根据业务进行数据集市与主维度模型的设计与建设，规范数据...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>3171</td>\n",
       "      <td>3346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>苏州瑞鹏信息技术有限公司</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        1、日常数据监控，包括日报设计、开发、维护等，完善数据报表体系，及时准确...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>3172</td>\n",
       "      <td>3347</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>同程艺龙</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,旅游</td>\n",
       "      <td>5.5</td>\n",
       "      <td>应届毕业生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"最佳雇主\",\"爱旅游\",\"免息贷款\",\"年底双薪\"]</td>\n",
       "      <td>\\n数据分析实习生职位要求：1）计算机、统计学、数学等相关专业，要求实习时间不短于6个月2）...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>3173</td>\n",
       "      <td>3348</td>\n",
       "      <td>专利数据分析师</td>\n",
       "      <td>智慧芽</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>20.0</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]</td>\n",
       "      <td>\\n        工作职责： \\n1. 深度分析专利文本，定出专利文本加工（抽取，分析） ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>3174</td>\n",
       "      <td>3349</td>\n",
       "      <td>游戏数据分析师</td>\n",
       "      <td>友谊时光</td>\n",
       "      <td>苏州</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网,游戏</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、协助产品监控和跟踪游戏数据，出具敏捷报告；\\n2、对游...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>3175</td>\n",
       "      <td>3350</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>准雀Accunique</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>硕士</td>\n",
       "      <td>未融资</td>\n",
       "      <td>数据服务,其他</td>\n",
       "      <td>15.0</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年终分红\",\"绩效奖金\",\"专项奖金\",\"带薪年假\"]</td>\n",
       "      <td>\\n客户需求：科研数据分析、程序设计、疑难专业问题解答等需求（包括但不限于经济、金融、心理、...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>3176</td>\n",
       "      <td>3351</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>瑞小云</td>\n",
       "      <td>苏州</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"专项奖金\",\"股票期权\",\"年度旅游\"]</td>\n",
       "      <td>\\n职位描述1.用户运营数据分析支撑，通过用户特征、行为数据、产品转化数据等对业务进行挖掘和...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>3177</td>\n",
       "      <td>3352</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>华泛信息</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网,其他</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"节日礼物\",\"带薪年假\",\"绩效奖金\"]</td>\n",
       "      <td>\\n岗位职责：\\n深入了解项目组的业务需求,在此基础上进行数据收集、数据分析、商业报告的撰写...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>3178</td>\n",
       "      <td>3353</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>德融嘉信</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责：\\n1. 开展业务专题分析，使用数据挖掘各类算法构建相关的业务模型，完成业务分...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>3404</td>\n",
       "      <td>3580</td>\n",
       "      <td>ETL/大数据/数据分析/实施</td>\n",
       "      <td>格蒂电力</td>\n",
       "      <td>天津</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>未融资</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n工作职责\\n1.   负责数据接入、数据整合中的链路配置与调度配置工作。\\n职位要求\\n...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>3405</td>\n",
       "      <td>3581</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>吉城美家</td>\n",
       "      <td>天津</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"五险一金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n负责站点日常数据分析、提前通过数据分析对业务有预测性、通过数据说话、解决站点管理问题、\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>3406</td>\n",
       "      <td>3582</td>\n",
       "      <td>数据分析支持</td>\n",
       "      <td>云链供应链</td>\n",
       "      <td>天津</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融,企业服务</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责部门日常数据报表的制定、维护、优化；\\n2、支持运...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1652 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      level_0  index     positionName companyShortName city companySize  \\\n",
       "0           0      0          高级数据分析师              拉勾网   北京   500-2000人   \n",
       "1           1      1            数据分析师         OK Group   北京   500-2000人   \n",
       "2           2      2          高级数据分析师           金山办公软件   北京     2000人以上   \n",
       "3           3      3            数据分析师           金山办公软件   北京     2000人以上   \n",
       "4           4      4            数据分析师             京东集团   北京     2000人以上   \n",
       "5           5      5            数据分析师           鲸鱼外教培优   北京   500-2000人   \n",
       "6           6      6             数据分析             北银消费   北京    150-500人   \n",
       "7           7      7            数据分析师   时趣social-touch   北京   500-2000人   \n",
       "8           8      8            数据分析师             e城e家   北京     2000人以上   \n",
       "9           9      9           数据分析专员          钛氪新媒体科技   北京     50-150人   \n",
       "10         10     10           数据分析专家             伊澳科技   北京     2000人以上   \n",
       "11         11     11            数据分析师             数数科技   北京      15-50人   \n",
       "12         12     12            数据分析师             省钱快报   北京    150-500人   \n",
       "13         13     13             数据分析             省钱快报   北京    150-500人   \n",
       "14         14     14            数据分析师             蛋壳公寓   北京     2000人以上   \n",
       "15         15     16           数据分析专员          钛氪新媒体科技   北京     50-150人   \n",
       "16         16     17            数据分析师             小帮规划   北京   500-2000人   \n",
       "17         17     18            数据分析师             美图公司   北京     2000人以上   \n",
       "18         18     21            数据分析师             魔力耳朵   北京   500-2000人   \n",
       "19         19     22          高级数据分析师               用友   北京   500-2000人   \n",
       "20         20     23            数据分析师              跟谁学   北京     2000人以上   \n",
       "21         21     24             数据分析               最右   北京   500-2000人   \n",
       "22         22     25          数据分析实习生             琥珀创想   北京     50-150人   \n",
       "23         23     26      数据分析师（教育产品）              跟谁学   北京     2000人以上   \n",
       "24         24     27            数据分析师          ZMT众盟数据   北京   500-2000人   \n",
       "25         25     29          高级数据分析师               探探   北京    150-500人   \n",
       "26         26     30            数据分析师             海捷科技   北京     50-150人   \n",
       "27         27     31            数据分析师           盛业恒泰投资   北京     50-150人   \n",
       "28         28     32            数据分析师               易观   北京    150-500人   \n",
       "29         29     33          高级数据分析师               易观   北京    150-500人   \n",
       "...       ...    ...              ...              ...  ...         ...   \n",
       "1622     2884   3055     数据分析师（食宿+五险）             潭州教育   长沙     2000人以上   \n",
       "1623     2885   3056  数据分析实习生(J10694)       树根互联技术有限公司   长沙    150-500人   \n",
       "1624     2886   3057            数据分析师               数魔   长沙     50-150人   \n",
       "1625     2887   3058           数据分析主管            益丰大药房   长沙     2000人以上   \n",
       "1626     2888   3059             数据分析             南京汇龙   长沙    150-500人   \n",
       "1627     2889   3060            数据分析师            233网校   长沙    150-500人   \n",
       "1628     2890   3061             数据分析         北京天意飞扬科技   长沙       少于15人   \n",
       "1629     2891   3062             数据分析          蜜獾律师事务所   长沙     50-150人   \n",
       "1630     2892   3063            数据分析师             蜜獾信息   长沙    150-500人   \n",
       "1631     2893   3064          bi数据分析师            武汉盛博汇   长沙    150-500人   \n",
       "1632     2894   3065  数据分析师中级（SC0902）             蜜獾信息   长沙    150-500人   \n",
       "1633     2895   3066           大数据分析师             政法频道   长沙    150-500人   \n",
       "1634     3164   3339          数据分析工程师              中地行   苏州     50-150人   \n",
       "1635     3165   3340          数据分析工程师             神彩科技   苏州    150-500人   \n",
       "1636     3166   3341            数据分析师             同程旅行   苏州     2000人以上   \n",
       "1637     3167   3342          高级数据分析师           K2DATA   苏州    150-500人   \n",
       "1638     3168   3343           数据分析助理          美能华智能科技   苏州      15-50人   \n",
       "1639     3169   3344      数据分析工程师（银行）              中地行   苏州     50-150人   \n",
       "1640     3170   3345            数据分析岗           昆山农商银行   苏州   500-2000人   \n",
       "1641     3171   3346            数据分析师     苏州瑞鹏信息技术有限公司   苏州     50-150人   \n",
       "1642     3172   3347            数据分析师             同程艺龙   苏州     2000人以上   \n",
       "1643     3173   3348          专利数据分析师              智慧芽   苏州    150-500人   \n",
       "1644     3174   3349          游戏数据分析师             友谊时光   苏州   500-2000人   \n",
       "1645     3175   3350            数据分析师      准雀Accunique   苏州     2000人以上   \n",
       "1646     3176   3351            数据分析师              瑞小云   苏州      15-50人   \n",
       "1647     3177   3352            数据分析师             华泛信息   苏州     2000人以上   \n",
       "1648     3178   3353           大数据分析师             德融嘉信   苏州     50-150人   \n",
       "1649     3404   3580  ETL/大数据/数据分析/实施             格蒂电力   天津   500-2000人   \n",
       "1650     3405   3581            数据分析师             吉城美家   天津     2000人以上   \n",
       "1651     3406   3582           数据分析支持            云链供应链   天津      15-50人   \n",
       "\n",
       "     education financeStage industryField  salary workYear  \\\n",
       "0           本科        D轮及以上          企业服务    30.0    5-10年   \n",
       "1           大专           B轮            金融    35.0    5-10年   \n",
       "2           本科         上市公司         移动互联网    20.0     3-5年   \n",
       "3           本科         上市公司         移动互联网    20.0     1-3年   \n",
       "4           本科         上市公司            电商    22.5     3-5年   \n",
       "5           本科           B轮            教育    22.5     3-5年   \n",
       "6           本科        不需要融资            金融    17.5     1-3年   \n",
       "7           本科        D轮及以上         移动互联网    17.5     3-5年   \n",
       "8           本科        不需要融资    移动互联网,消费生活    20.0     3-5年   \n",
       "9           本科          未融资         移动互联网    12.0     1-3年   \n",
       "10          本科         上市公司      移动互联网,电商    45.0    5-10年   \n",
       "11          本科           A轮       游戏,数据服务    15.0     1-3年   \n",
       "12          本科           C轮      移动互联网,电商    30.0     3-5年   \n",
       "13          本科           C轮      移动互联网,电商    30.0     3-5年   \n",
       "14          本科         上市公司          消费生活    22.5     3-5年   \n",
       "15          本科          未融资         移动互联网    12.0     1-3年   \n",
       "16          本科           B轮      移动互联网,金融    35.0     3-5年   \n",
       "17          本科         上市公司            硬件    35.0     3-5年   \n",
       "18          本科           B轮            教育    11.5     1-3年   \n",
       "19          本科         上市公司    移动互联网,数据服务    24.0     3-5年   \n",
       "20          本科         上市公司      移动互联网,教育    20.0     3-5年   \n",
       "21          本科        D轮及以上         文娱丨内容    22.5     1-3年   \n",
       "22          本科           A轮         移动互联网     4.5    应届毕业生   \n",
       "23          本科         上市公司      移动互联网,教育    22.5     3-5年   \n",
       "24          大专           C轮    移动互联网,数据服务    13.5     1-3年   \n",
       "25          本科        D轮及以上      移动互联网,社交    35.0     3-5年   \n",
       "26          不限        不需要融资    移动互联网,数据服务    15.0       不限   \n",
       "27          不限          未融资            金融    11.5       不限   \n",
       "28          本科           B轮    移动互联网,数据服务    37.5     3-5年   \n",
       "29          本科           B轮    移动互联网,数据服务    37.5    5-10年   \n",
       "...        ...          ...           ...     ...      ...   \n",
       "1622        大专           B轮            教育     4.5     1-3年   \n",
       "1623        不限           A轮    移动互联网,企业服务     4.0    应届毕业生   \n",
       "1624        本科          天使轮       电商,数据服务    15.0     3-5年   \n",
       "1625        大专         上市公司      电商,医疗丨健康     7.5     3-5年   \n",
       "1626        本科          未融资         移动互联网     6.0     1-3年   \n",
       "1627        本科          天使轮      移动互联网,教育     9.0     3-5年   \n",
       "1628        不限          未融资      电商,移动互联网    10.0     1-3年   \n",
       "1629        本科          未融资            其他    10.5     3-5年   \n",
       "1630        本科        不需要融资         移动互联网     6.0     1-3年   \n",
       "1631        大专        不需要融资   医疗丨健康,移动互联网     8.5     1-3年   \n",
       "1632        本科        不需要融资         移动互联网     8.0     1-3年   \n",
       "1633        硕士        不需要融资            其他    22.5    5-10年   \n",
       "1634        本科        不需要融资    数据服务,移动互联网    11.5     1-3年   \n",
       "1635        本科        不需要融资          数据服务    10.0     1-3年   \n",
       "1636        本科         上市公司            旅游    22.5     3-5年   \n",
       "1637        本科           B轮          数据服务    22.5     3-5年   \n",
       "1638        大专           A轮     企业服务,人工智能     5.0       不限   \n",
       "1639        本科        不需要融资    数据服务,移动互联网    18.0       不限   \n",
       "1640        本科          未融资            金融    18.0     1-3年   \n",
       "1641        硕士        不需要融资    移动互联网,数据服务    18.0     1-3年   \n",
       "1642        本科         上市公司      移动互联网,旅游     5.5    应届毕业生   \n",
       "1643        本科        D轮及以上         移动互联网    20.0       不限   \n",
       "1644        本科         上市公司      移动互联网,游戏    11.5     1-3年   \n",
       "1645        硕士          未融资       数据服务,其他    15.0       不限   \n",
       "1646        本科        不需要融资         移动互联网    15.0     3-5年   \n",
       "1647        大专        不需要融资      移动互联网,其他    12.5     1-3年   \n",
       "1648        本科        不需要融资         移动互联网     9.0     1-3年   \n",
       "1649        大专          未融资          企业服务     9.0     3-5年   \n",
       "1650        本科          未融资         移动互联网    10.5     1-3年   \n",
       "1651        本科          未融资       金融,企业服务     5.0     1-3年   \n",
       "\n",
       "                                                 hitags  \\\n",
       "0     [\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...   \n",
       "1                                                   NaN   \n",
       "2                                                   NaN   \n",
       "3                                                   NaN   \n",
       "4                                [\"免费班车\",\"免费体检\",\"地铁周边\"]   \n",
       "5                                                   NaN   \n",
       "6                                                   NaN   \n",
       "7                                                   NaN   \n",
       "8                                                   NaN   \n",
       "9                                                   NaN   \n",
       "10                                                  NaN   \n",
       "11                                                  NaN   \n",
       "12                                                  NaN   \n",
       "13                                                  NaN   \n",
       "14                            [\"试用期上公积金\",\"地铁周边\",\"6险1金\"]   \n",
       "15                                                  NaN   \n",
       "16                                                  NaN   \n",
       "17                                                  NaN   \n",
       "18                                                  NaN   \n",
       "19                                                  NaN   \n",
       "20                                                  NaN   \n",
       "21                                                  NaN   \n",
       "22                                                  NaN   \n",
       "23                                                  NaN   \n",
       "24                                                  NaN   \n",
       "25                                                  NaN   \n",
       "26                                                  NaN   \n",
       "27                                                  NaN   \n",
       "28                                      [\"早十晚七\",\"生日聚会\"]   \n",
       "29                                      [\"早十晚七\",\"生日聚会\"]   \n",
       "...                                                 ...   \n",
       "1622                                                NaN   \n",
       "1623                                                NaN   \n",
       "1624                                                NaN   \n",
       "1625                                                NaN   \n",
       "1626                                                NaN   \n",
       "1627                                                NaN   \n",
       "1628                                                NaN   \n",
       "1629                                                NaN   \n",
       "1630                                                NaN   \n",
       "1631                                                NaN   \n",
       "1632                                                NaN   \n",
       "1633                                                NaN   \n",
       "1634                     [\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]   \n",
       "1635                                                NaN   \n",
       "1636                                                NaN   \n",
       "1637                                                NaN   \n",
       "1638                                                NaN   \n",
       "1639                     [\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]   \n",
       "1640                                                NaN   \n",
       "1641                                                NaN   \n",
       "1642                                                NaN   \n",
       "1643                                                NaN   \n",
       "1644                                                NaN   \n",
       "1645                                                NaN   \n",
       "1646                                                NaN   \n",
       "1647                                                NaN   \n",
       "1648                                                NaN   \n",
       "1649                                                NaN   \n",
       "1650                                                NaN   \n",
       "1651                                                NaN   \n",
       "\n",
       "                     companyLabelList  \\\n",
       "0       [\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]   \n",
       "1        [\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]   \n",
       "2       [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "3       [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "4       [\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]   \n",
       "5       [\"交通补助\",\"绩效奖金\",\"带薪年假\",\"节日礼物\"]   \n",
       "6       [\"节日礼物\",\"技能培训\",\"绩效奖金\",\"岗位晋升\"]   \n",
       "7       [\"弹性工作\",\"年终奖金\",\"个性福利\",\"带薪年假\"]   \n",
       "8       [\"年底双薪\",\"绩效奖金\",\"带薪年假\",\"午餐补助\"]   \n",
       "9                                  []   \n",
       "10      [\"专项奖金\",\"股票期权\",\"绩效奖金\",\"年终分红\"]   \n",
       "11      [\"节日礼物\",\"股票期权\",\"带薪年假\",\"年终分红\"]   \n",
       "12      [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]   \n",
       "13      [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]   \n",
       "14      [\"技能培训\",\"带薪年假\",\"岗位晋升\",\"年度旅游\"]   \n",
       "15                                 []   \n",
       "16      [\"五险一金\",\"午餐补助\",\"交通补助\",\"带薪年假\"]   \n",
       "17     [\"节日礼物\",\"与大牛共事\",\"福利健全\",\"五险一金\"]   \n",
       "18       [\"带薪年假\",\"弹性工作\",\"节日礼物\",\"领导好\"]   \n",
       "19                                 []   \n",
       "20      [\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]   \n",
       "21    [\"技能培训\",\"连续创业团队\",\"年度旅游\",\"扁平管理\"]   \n",
       "22     [\"绩效奖金\",\"提供工作餐\",\"定期团建\",\"交通补助\"]   \n",
       "23      [\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]   \n",
       "24      [\"股票期权\",\"专项奖金\",\"绩效奖金\",\"五险一金\"]   \n",
       "25      [\"股票期权\",\"带薪年假\",\"岗位晋升\",\"扁平管理\"]   \n",
       "26      [\"定期体检\",\"带薪年假\",\"年度旅游\",\"节日礼物\"]   \n",
       "27                                 []   \n",
       "28      [\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]   \n",
       "29      [\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]   \n",
       "...                               ...   \n",
       "1622                               []   \n",
       "1623       [\"专项奖金\",\"帅哥多\",\"领导好\",\"美女多\"]   \n",
       "1624     [\"年底双薪\",\"带薪年假\",\"年度旅游\",\"领导好\"]   \n",
       "1625    [\"年底双薪\",\"绩效奖金\",\"五险一金\",\"免费午餐\"]   \n",
       "1626                               []   \n",
       "1627    [\"节日礼物\",\"年底双薪\",\"技能培训\",\"岗位晋升\"]   \n",
       "1628                               []   \n",
       "1629                               []   \n",
       "1630    [\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1631                               []   \n",
       "1632    [\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1633                               []   \n",
       "1634    [\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]   \n",
       "1635     [\"带薪年假\",\"管吃\",\"领导好\",\"全额五险一金\"]   \n",
       "1636    [\"绩效奖金\",\"股票期权\",\"年底双薪\",\"五险一金\"]   \n",
       "1637   [\"风口行业\",\"顶尖团队\",\"扁平管理\",\"学习型组织\"]   \n",
       "1638                               []   \n",
       "1639    [\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]   \n",
       "1640    [\"专项奖金\",\"午餐补助\",\"节日礼物\",\"技能培训\"]   \n",
       "1641                               []   \n",
       "1642     [\"最佳雇主\",\"爱旅游\",\"免息贷款\",\"年底双薪\"]   \n",
       "1643    [\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]   \n",
       "1644    [\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]   \n",
       "1645    [\"年终分红\",\"绩效奖金\",\"专项奖金\",\"带薪年假\"]   \n",
       "1646    [\"绩效奖金\",\"专项奖金\",\"股票期权\",\"年度旅游\"]   \n",
       "1647    [\"技能培训\",\"节日礼物\",\"带薪年假\",\"绩效奖金\"]   \n",
       "1648                               []   \n",
       "1649    [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"岗位晋升\"]   \n",
       "1650                  [\"五险一金\",\"岗位晋升\"]   \n",
       "1651                               []   \n",
       "\n",
       "                                             job_detail  Python  SQL  Tableau  \\\n",
       "0     \\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...       1    1        0   \n",
       "1     \\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...       0    1        0   \n",
       "2     \\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...       1    1        0   \n",
       "3     \\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...       0    1        0   \n",
       "4     \\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...       0    1        1   \n",
       "5     \\n        1.负责业务数据分析工作，基于业务场景，建立核心业务数据模型及预警体系；...       0    0        0   \n",
       "6     \\n        职责描述：\\n1、对公司业务数据进行分析，提出有效合理的风控建议；\\n2...       0    0        0   \n",
       "7     \\n        岗位描述：\\n品牌舆情，社交分析，服务过品牌客户，发布周期性评估报告（美...       0    1        0   \n",
       "8     \\n        技能要求：\\n数据分析，sql，数据挖掘，需求分析，商业数据分析，sps...       1    1        0   \n",
       "9     \\n        职位职责：\\n1、负责inews新闻大数据产品数据分析，提出数据问题、分...       1    1        0   \n",
       "10    \\n        职位描述\\n1.基于对业务深入理解的基础上，搭建业务分析模型，为业务提供...       0    0        0   \n",
       "11    \\n        职位描述：\\n\\n1. 负责数据后台的实施落地，引导客户了解产品使用方法...       0    0        0   \n",
       "12    \\n        职位描述：\\n1、负责搜索和推荐业务的数据分析工作；\\n2、基于内外部的...       1    1        0   \n",
       "13    \\n        职位描述： \\n1、负责搜索和推荐业务的数据分析工作 \\n2、基于内外部...       1    1        0   \n",
       "14    \\n岗位职责：\\n1、深入洞察行业、用户、业务背景，分析核心数据，制定科学目标；\\n2、建立...       1    1        0   \n",
       "15    \\n\\n\\n\\n职位职责：\\n\\n1、负责inews新闻大数据产品的数据分析、挖掘、监控、整...       1    1        0   \n",
       "16    \\n        这个职位需要做什么？\\n1. 业务数据分析体系优化：包含但不限于业务数据...       0    0        0   \n",
       "17    \\n        岗位职责： \\n\\n1、关注市场动态，做行业深度分析，定期产出行业以及竞...       0    0        0   \n",
       "18    \\n        【岗位职责】\\n1.基于对教学服务深入理解的基础上，搭建业务分析模型，统...       0    0        0   \n",
       "19    \\n数据分析师 岗位职责:1、 负责政府数据产品和项目中的数据治理、数据分析、数据展示工作；...       1    0        0   \n",
       "20    \\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...       0    1        0   \n",
       "21    \\n职责:\\n1.对最右产品运营数据进行分析、监测、统计，并形成指标体系；\\n2.制定业务核...       1    1        0   \n",
       "22    \\n工作内容： \\n1、负责竞品相关数据监控，定期更新数据，形成相关报表；\\n2、负责对异常...       1    1        0   \n",
       "23    \\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...       0    1        0   \n",
       "24    \\n        岗位职责：\\n 1、负责每日部门日常数据报表与分析指标；\\n  2、负责...       0    1        0   \n",
       "25    \\n职位职责：\\n1、整体负责探探下产品业务线的各项数据分析相关工作\\n2、协助构建新的数据...       0    1        1   \n",
       "26    \\n        岗位职责：\\n1、根据业务需求，充分利用现有数据资源，进行数据提取、整理...       0    0        1   \n",
       "27    \\n        一 、岗位职责\\n1，搜集行业相关信息，为相关需求者提供更准确的数据信息...       0    0        0   \n",
       "28    \\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...       0    1        0   \n",
       "29    \\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...       0    1        0   \n",
       "...                                                 ...     ...  ...      ...   \n",
       "1622  \\n        岗位职责：\\n1.负责部门推广运营及多渠道业务数据的整理和分析，制作数据...       0    0        0   \n",
       "1623  \\n        工作职责:\\n(1)协助项业务需求和文档梳理工作；\\n(2)协助算法工程...       1    0        0   \n",
       "1624  \\n        位职责：\\n1、完成因项目需要而开展的数据处理、整合，参与算法模型的开发...       1    1        1   \n",
       "1625  \\n        岗位职责：\\n1、提取商品销售等数据，进行数据分析，制作数据报表及数据可...       0    0        0   \n",
       "1626  \\n数据岗位要求：（工作地点：长沙 ）\\n工作要求\\n1、电信移动用户维系相关数据统计及分析...       1    1        1   \n",
       "1627  \\n岗位职责： 1、优化运营数据分析体系，帮助确定各项运营数据指标； 2、配合业务部门给予数...       1    1        0   \n",
       "1628  \\n1、能熟练使用sql分析数据，熟练使用数理统计、数据分析、数据挖掘工具软件（sas、r、...       1    1        1   \n",
       "1629  \\n岗位职责： 1.负责业务相关数据的获取、清洗、分析、挖掘和可视化，从数据收集/准备到模型...       1    1        0   \n",
       "1630  \\n岗位职责：\\n1． 熟悉公司产品，对公司产品相关的业务数据进行分析、整理，产出基于数据的...       0    1        0   \n",
       "1631  \\n        岗位职责：\\n1、正确理解业务，完成数据仓库主题设计，核心表设计，各类源...       0    1        0   \n",
       "1632  \\n工作职责:1.与中国及美国总部的it部门，和业务部门紧密协作，搜集专业的房产及交易市场信...       0    1        0   \n",
       "1633  \\n岗位职责：\\n负责新闻资讯、用户行为数据等海量数据采集、处理、存储，应用方案的技术选型及...       1    0        0   \n",
       "1634  \\n岗位描述：1、负责银行数据分析模型的建立、调优和迭代；2、负责银行数据的清洗加工；3、负...       1    1        0   \n",
       "1635  \\n岗位要求：\\n本科及以上学历，计算机、数学、统计学；\\n1.具有丰富的sql经验；\\n2...       1    1        1   \n",
       "1636  \\n工作职责：\\n1、本地生活业务数据分析，订单、营收、用户分级、留存、运营等策略方向分析；...       0    1        0   \n",
       "1637  \\n岗位说明：\\n1. 针对重点工业行业（能源，石化，汽车，钢铁、轮胎等）的智能制造与工业互...       1    0        0   \n",
       "1638  \\n        岗位职责：\\n1、负责数据的整理和分析、为人工智能模型提供文字的标注数据...       0    0        0   \n",
       "1639  \\n        岗位职责：\\n1、负责银行数据分析模型的建立、调优和迭代；\\n2、负责银...       1    1        0   \n",
       "1640  \\n        工作职责:\\n1、根据业务进行数据集市与主维度模型的设计与建设，规范数据...       1    1        0   \n",
       "1641  \\n        1、日常数据监控，包括日报设计、开发、维护等，完善数据报表体系，及时准确...       1    1        0   \n",
       "1642  \\n数据分析实习生职位要求：1）计算机、统计学、数学等相关专业，要求实习时间不短于6个月2）...       0    1        0   \n",
       "1643  \\n        工作职责： \\n1. 深度分析专利文本，定出专利文本加工（抽取，分析） ...       1    1        0   \n",
       "1644  \\n        岗位职责：\\n1、协助产品监控和跟踪游戏数据，出具敏捷报告；\\n2、对游...       1    1        0   \n",
       "1645  \\n客户需求：科研数据分析、程序设计、疑难专业问题解答等需求（包括但不限于经济、金融、心理、...       1    0        0   \n",
       "1646  \\n职位描述1.用户运营数据分析支撑，通过用户特征、行为数据、产品转化数据等对业务进行挖掘和...       1    1        0   \n",
       "1647  \\n岗位职责：\\n深入了解项目组的业务需求,在此基础上进行数据收集、数据分析、商业报告的撰写...       1    1        0   \n",
       "1648  \\n岗位职责：\\n1. 开展业务专题分析，使用数据挖掘各类算法构建相关的业务模型，完成业务分...       1    1        0   \n",
       "1649  \\n工作职责\\n1.   负责数据接入、数据整合中的链路配置与调度配置工作。\\n职位要求\\n...       0    1        0   \n",
       "1650    \\n负责站点日常数据分析、提前通过数据分析对业务有预测性、通过数据说话、解决站点管理问题、\\n       0    0        0   \n",
       "1651  \\n        岗位职责：\\n1、负责部门日常数据报表的制定、维护、优化；\\n2、支持运...       0    0        0   \n",
       "\n",
       "      Excel  SPSS/SAS  \n",
       "0         0         0  \n",
       "1         0         0  \n",
       "2         0         0  \n",
       "3         0         1  \n",
       "4         1         1  \n",
       "5         0         0  \n",
       "6         1         1  \n",
       "7         1         0  \n",
       "8         1         1  \n",
       "9         1         0  \n",
       "10        0         0  \n",
       "11        0         0  \n",
       "12        0         0  \n",
       "13        0         0  \n",
       "14        1         0  \n",
       "15        1         0  \n",
       "16        0         0  \n",
       "17        0         0  \n",
       "18        0         1  \n",
       "19        1         0  \n",
       "20        1         0  \n",
       "21        1         0  \n",
       "22        1         0  \n",
       "23        1         0  \n",
       "24        1         0  \n",
       "25        0         0  \n",
       "26        0         0  \n",
       "27        0         0  \n",
       "28        0         0  \n",
       "29        0         0  \n",
       "...     ...       ...  \n",
       "1622      1         0  \n",
       "1623      0         0  \n",
       "1624      1         0  \n",
       "1625      0         0  \n",
       "1626      0         1  \n",
       "1627      0         1  \n",
       "1628      0         1  \n",
       "1629      0         0  \n",
       "1630      1         0  \n",
       "1631      0         0  \n",
       "1632      0         0  \n",
       "1633      0         0  \n",
       "1634      0         1  \n",
       "1635      1         1  \n",
       "1636      1         0  \n",
       "1637      0         0  \n",
       "1638      0         0  \n",
       "1639      0         1  \n",
       "1640      0         0  \n",
       "1641      0         1  \n",
       "1642      1         1  \n",
       "1643      0         0  \n",
       "1644      0         0  \n",
       "1645      0         1  \n",
       "1646      0         0  \n",
       "1647      1         0  \n",
       "1648      1         1  \n",
       "1649      0         0  \n",
       "1650      0         0  \n",
       "1651      0         0  \n",
       "\n",
       "[1652 rows x 19 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historical-trail",
   "metadata": {},
   "source": [
    "### 行业信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "regulation-accountability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      企业服务\n",
       "1        金融\n",
       "2     移动互联网\n",
       "3     移动互联网\n",
       "4        电商\n",
       "5        教育\n",
       "6        金融\n",
       "7     移动互联网\n",
       "8      消费生活\n",
       "9     移动互联网\n",
       "10       电商\n",
       "11       游戏\n",
       "12       电商\n",
       "13       电商\n",
       "14     消费生活\n",
       "15    移动互联网\n",
       "16       金融\n",
       "17       硬件\n",
       "18       教育\n",
       "19     数据服务\n",
       "20       教育\n",
       "21    文娱丨内容\n",
       "22    移动互联网\n",
       "23       教育\n",
       "24     数据服务\n",
       "25       社交\n",
       "26     数据服务\n",
       "27       金融\n",
       "28     数据服务\n",
       "29     数据服务\n",
       "Name: industryField, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert(x):\n",
    "    field = x.split(',')\n",
    "    if (field[0] == '移动互联网') & (len(field)>1):\n",
    "        return field[1]\n",
    "    else:\n",
    "        return field[0]\n",
    "job['industryField']=job.industryField.map(convert)\n",
    "job['industryField'][:30]                         "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "urban-lebanon",
   "metadata": {},
   "source": [
    "### 各城市对数据分析岗位的需求量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rental-museum",
   "metadata": {},
   "source": [
    "两种常⽤颜⾊：浅蓝⾊： #3c7f99 ，淡⻩⾊：#c5b783"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "appointed-canyon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "北京    403\n",
       "上海    396\n",
       "深圳    289\n",
       "广州    212\n",
       "杭州    134\n",
       "成都     91\n",
       "南京     33\n",
       "武汉     23\n",
       "厦门     22\n",
       "长沙     20\n",
       "苏州     15\n",
       "西安     11\n",
       "天津      3\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "data = job.city.value_counts()  #这个好！！！以城市为对象，计算各城市数量！！！！\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "received-friendly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '各城市数据分析师岗位需求量')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.barh(y = data.index[::-1],width = data[::-1],color = '#3c7f99')#-1表示倒序排列\n",
    "plt.rcParams['font.sans-serif']=['STSong'] #显示中文\n",
    "#https://www.cnblogs.com/doudou618/p/4455585.html  ------十六进制颜色码\n",
    "plt.box(False)  #去掉边框\n",
    "plt.grid(axis='x',color = '#3c7f99')  #网格线  ---加横轴网格线\n",
    "\n",
    "plt.title(label='各城市数据分析师岗位需求量',\n",
    "          fontsize=22,\n",
    "          backgroundcolor = '#c5b783',\n",
    "          color = 'white',\n",
    "          weight = 'bold',\n",
    "          pad = 30,#标题和上边界的间隔\n",
    "          fontfamily = 'KaiTi'\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impressed-anger",
   "metadata": {},
   "source": [
    "### 不同领域对数据分析师需求情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ideal-adobe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '     各领域数据分析师需求量      ')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = job.industryField.value_counts()[:10]\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.barh(y = data.index[::-1],width=data[::-1],color = '#3c7f99')\n",
    "plt.grid(linestyle='--',color = '#3c7f99',axis='x')\n",
    "\n",
    "plt.title(label='     各领域数据分析师需求量      ',fontsize=20,color = 'white',\n",
    "         backgroundcolor= '#c5b783',pad=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limited-scheme",
   "metadata": {},
   "source": [
    "### 各城市薪资状况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "theoretical-leonard",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salary = job.groupby(by = 'city')['salary'].mean().sort_values()\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.bar(x=salary.index,\n",
    "        height=salary,\n",
    "        color = plt.cm.RdBu_r(np.linspace(0,1,salary.size)))\n",
    "\n",
    "plt.title(label='     各城市薪资状况     ',fontsize=20,\n",
    "         color='white',backgroundcolor='#c5b783',weight = 'bold',pad = 30)\n",
    "\n",
    "plt.box(False)\n",
    "plt.grid(axis = 'y',color = 'k')#网格线\n",
    "\n",
    "plt.tick_params(labelsize = 18,rotation=60)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conditional-language",
   "metadata": {},
   "source": [
    "### 各城市工作年限与薪水关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "wicked-latter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>workYear</th>\n",
       "      <th>1-3年</th>\n",
       "      <th>10年以上</th>\n",
       "      <th>1年以下</th>\n",
       "      <th>3-5年</th>\n",
       "      <th>5-10年</th>\n",
       "      <th>不限</th>\n",
       "      <th>应届毕业生</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>16.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>16.9</td>\n",
       "      <td>42.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>24.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>南京</th>\n",
       "      <td>10.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>61.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>厦门</th>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.8</td>\n",
       "      <td>28.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>11.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>19.3</td>\n",
       "      <td>27.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成都</th>\n",
       "      <td>9.6</td>\n",
       "      <td>32.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>10.9</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>15.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>武汉</th>\n",
       "      <td>10.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>16.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>17.1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>苏州</th>\n",
       "      <td>12.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西安</th>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长沙</th>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "      <td>18.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "workYear  1-3年  10年以上  1年以下  3-5年  5-10年    不限  应届毕业生\n",
       "city                                                 \n",
       "上海        16.6    NaN  16.1  22.9   31.4  15.7    7.1\n",
       "北京        16.9   42.5   9.9  24.7   30.8  17.3    8.1\n",
       "南京        10.4    NaN  12.5  17.3   61.6   9.2    6.8\n",
       "厦门        13.5    NaN   NaN  19.8   28.2  15.8   18.0\n",
       "天津         7.8    NaN   NaN   9.0    NaN   NaN    NaN\n",
       "广州        11.7    NaN   6.1  19.3   27.4  11.1    7.5\n",
       "成都         9.6   32.5   6.5  14.6   27.5  10.9    6.7\n",
       "杭州        15.3   85.0   NaN  22.2   29.8  17.2   12.1\n",
       "武汉        10.9    NaN   NaN  23.0   29.1   8.7    6.8\n",
       "深圳        16.6   45.0   9.2  22.1   31.5  17.1    8.0\n",
       "苏州        12.9    NaN   NaN  20.0    NaN  14.5    5.5\n",
       "西安         5.8    NaN   NaN  12.0   14.0  30.0   11.5\n",
       "长沙         9.2    NaN   NaN  10.5   18.8   9.8    9.5"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#透视表，结果返回是个dataframe\n",
    "job.pivot_table(values = 'salary',index = 'city',columns = 'workYear').round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "behind-finding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>workYear</th>\n",
       "      <th>1-3年</th>\n",
       "      <th>10年以上</th>\n",
       "      <th>1年以下</th>\n",
       "      <th>3-5年</th>\n",
       "      <th>5-10年</th>\n",
       "      <th>不限</th>\n",
       "      <th>应届毕业生</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>南京</th>\n",
       "      <td>10.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>61.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>16.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>17.1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>16.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>16.9</td>\n",
       "      <td>42.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>24.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>15.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>武汉</th>\n",
       "      <td>10.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>厦门</th>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.8</td>\n",
       "      <td>28.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成都</th>\n",
       "      <td>9.6</td>\n",
       "      <td>32.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>10.9</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>11.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>19.3</td>\n",
       "      <td>27.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长沙</th>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "      <td>18.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西安</th>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>苏州</th>\n",
       "      <td>12.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "workYear  1-3年  10年以上  1年以下  3-5年  5-10年    不限  应届毕业生\n",
       "city                                                 \n",
       "南京        10.4    NaN  12.5  17.3   61.6   9.2    6.8\n",
       "深圳        16.6   45.0   9.2  22.1   31.5  17.1    8.0\n",
       "上海        16.6    NaN  16.1  22.9   31.4  15.7    7.1\n",
       "北京        16.9   42.5   9.9  24.7   30.8  17.3    8.1\n",
       "杭州        15.3   85.0   NaN  22.2   29.8  17.2   12.1\n",
       "武汉        10.9    NaN   NaN  23.0   29.1   8.7    6.8\n",
       "厦门        13.5    NaN   NaN  19.8   28.2  15.8   18.0\n",
       "成都         9.6   32.5   6.5  14.6   27.5  10.9    6.7\n",
       "广州        11.7    NaN   6.1  19.3   27.4  11.1    7.5\n",
       "长沙         9.2    NaN   NaN  10.5   18.8   9.8    9.5\n",
       "西安         5.8    NaN   NaN  12.0   14.0  30.0   11.5\n",
       "天津         7.8    NaN   NaN   9.0    NaN   NaN    NaN\n",
       "苏州        12.9    NaN   NaN  20.0    NaN  14.5    5.5"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分组操作，结果是个serious，两层索引\n",
    "years_salary = job.groupby(by = ['city','workYear'])['salary']\\\n",
    ".mean().round(1).unstack().sort_values(by = '5-10年',ascending=False)\n",
    "years_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "human-amsterdam",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>workYear</th>\n",
       "      <th>应届毕业生</th>\n",
       "      <th>1-3年</th>\n",
       "      <th>3-5年</th>\n",
       "      <th>5-10年</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>8.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>22.1</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>7.1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>8.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>24.7</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>12.1</td>\n",
       "      <td>15.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>武汉</th>\n",
       "      <td>6.8</td>\n",
       "      <td>10.9</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>厦门</th>\n",
       "      <td>18.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成都</th>\n",
       "      <td>6.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>14.6</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>7.5</td>\n",
       "      <td>11.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长沙</th>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.5</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西安</th>\n",
       "      <td>11.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>苏州</th>\n",
       "      <td>5.5</td>\n",
       "      <td>12.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "workYear  应届毕业生  1-3年  3-5年  5-10年\n",
       "city                              \n",
       "深圳          8.0  16.6  22.1   31.5\n",
       "上海          7.1  16.6  22.9   31.4\n",
       "北京          8.1  16.9  24.7   30.8\n",
       "杭州         12.1  15.3  22.2   29.8\n",
       "武汉          6.8  10.9  23.0   29.1\n",
       "厦门         18.0  13.5  19.8   28.2\n",
       "成都          6.7   9.6  14.6   27.5\n",
       "广州          7.5  11.7  19.3   27.4\n",
       "长沙          9.5   9.2  10.5   18.8\n",
       "西安         11.5   5.8  12.0   14.0\n",
       "天津          NaN   7.8   9.0    NaN\n",
       "苏州          5.5  12.9  20.0    NaN"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_salary = years_salary[['应届毕业生','1-3年','3-5年','5-10年']].iloc[1:] \n",
    "#由于南京有奇异值，可排除掉南京的数值---.iloc[1:] \n",
    "year_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "blocked-simulation",
   "metadata": {},
   "outputs": [],
   "source": [
    "nd = year_salary.values\n",
    "data = np.repeat(nd,4,axis = 1)#将复制4份，使数据增强，画图更加美观"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fresh-shipping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.imshow(data,cmap = plt.cm.RdBu_r)\n",
    "\n",
    "plt.xticks([1.5,5.5,9.5,13.5],year_salary.columns)\n",
    "plt.yticks(np.arange(12),year_salary.index)  #----如果排除掉南京，此处的13要改为12\n",
    "\n",
    "h,w = data.shape #h =13,13个城市，w = 16(4*4)\n",
    "for x in range(w):\n",
    "    for y in range(h):\n",
    "        if (x%4 == 0) and (~np.isnan(data[y,x])):#-----空数据不显示值\n",
    "            plt.text(x+1.5,y,data[y,x],ha = 'center',va='center')\n",
    "\n",
    "_ = plt.title(label='   工作经验和薪资关系    ',fontsize = 22,pad = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dress-jones",
   "metadata": {},
   "source": [
    "### 学历要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "cooperative-solid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科    1409\n",
       "硕士      88\n",
       "大专      79\n",
       "不限      76\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu = job['education'].value_counts()\n",
    "edu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "studied-wheel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '   数据分析师学历情况      ')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "_ = plt.pie(edu,autopct='%0.2f%%',labels=edu.index,\n",
    "           wedgeprops={'width':0.5},pctdistance= 0.75)\n",
    "plt.title(label = '   数据分析师学历情况      ',fontsize = 20,pad=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adaptive-chapter",
   "metadata": {},
   "source": [
    "### 技能要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "afraid-converter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond1 = job['Python'].astype('bool')\n",
    "cond2 = job['SQL'].astype('bool')\n",
    "cond3 = job['Excel'].astype('bool')\n",
    "cond4 = job['SPSS/SAS'].astype('bool')\n",
    "\n",
    "d1 = job[cond1]['salary']\n",
    "d2 = job[cond2]['salary']\n",
    "d3 = job[cond3]['salary']\n",
    "d4 = job[cond4]['salary']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "_ = plt.boxplot([d1,d2,d3,d4],vert = False,\n",
    "                \n",
    "                labels=['Python','SQL','Excel','SPSS/SAS'])#---vert转为水平方向\n",
    "\n",
    "plt.grid(axis='x',color = 'k',alpha = 0.3,linestyle='--')\n",
    "_ = plt.xticks(np.arange(0,161,step = 20),\n",
    "          [str(i)+'k' for i in np.arange(0,161,step = 20)]\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worst-boutique",
   "metadata": {},
   "source": [
    "### 大厂对技能要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "joint-perfume",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = job.companySize=='2000人以上'\n",
    "bc = job[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "configured-identifier",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = bc[['Python','SQL','Tableau','Excel','SPSS/SAS']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "logical-reader",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#ff0000', '#ffa500', '#c5b783', '#3c7f99', '#0000cd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "hungarian-reunion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.bar(x = np.arange(5), height=data,\n",
    "        tick_label=['Python','SQL','Tableau','Excel','SPSS/SAS'],color = colors,\n",
    "        width = 0.5)\n",
    "plt.title(label='   大公司对技能要求    ',fontsize = 20,pad=30)\n",
    "plt.grid(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "digital-liver",
   "metadata": {},
   "source": [
    "### 不同规模公司招人要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "unable-incentive",
   "metadata": {},
   "outputs": [],
   "source": [
    "workYear_map = {\n",
    " \"5-10年\": 5,\n",
    " \"3-5年\": 4,\n",
    " \"1-3年\": 3,\n",
    " \"1年以下\": 2,\n",
    " \"应届毕业⽣\": 1}\n",
    "color_map = {\n",
    " 5:\"#ff0000\",\n",
    " 4:\"#ffa500\",\n",
    " 3:\"#c5b783\",\n",
    " 2:\"#3c7f99\",\n",
    " 1:\"#0000cd\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "cross-latin",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = job.workYear.isin(workYear_map)\n",
    "job2 = job[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "transsexual-fishing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1652, 19)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1322, 19)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(job.shape,job2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "furnished-southeast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyShortName</th>\n",
       "      <th>city</th>\n",
       "      <th>companySize</th>\n",
       "      <th>education</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>industryField</th>\n",
       "      <th>salary</th>\n",
       "      <th>workYear</th>\n",
       "      <th>hitags</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>job_detail</th>\n",
       "      <th>Python</th>\n",
       "      <th>SQL</th>\n",
       "      <th>Tableau</th>\n",
       "      <th>Excel</th>\n",
       "      <th>SPSS/SAS</th>\n",
       "      <th>workYearNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>拉勾网</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...</td>\n",
       "      <td>[\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]</td>\n",
       "      <td>\\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>OK Group</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>B轮</td>\n",
       "      <td>金融</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]</td>\n",
       "      <td>\\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>金山办公软件</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>金山办公软件</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]</td>\n",
       "      <td>\\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"免费班车\",\"免费体检\",\"地铁周边\"]</td>\n",
       "      <td>[\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]</td>\n",
       "      <td>\\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>鲸鱼外教培优</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"交通补助\",\"绩效奖金\",\"带薪年假\",\"节日礼物\"]</td>\n",
       "      <td>\\n        1.负责业务数据分析工作，基于业务场景，建立核心业务数据模型及预警体系；...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北银消费</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"绩效奖金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n        职责描述：\\n1、对公司业务数据进行分析，提出有效合理的风控建议；\\n2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>时趣social-touch</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>17.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"弹性工作\",\"年终奖金\",\"个性福利\",\"带薪年假\"]</td>\n",
       "      <td>\\n        岗位描述：\\n品牌舆情，社交分析，服务过品牌客户，发布周期性评估报告（美...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>e城e家</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"绩效奖金\",\"带薪年假\",\"午餐补助\"]</td>\n",
       "      <td>\\n        技能要求：\\n数据分析，sql，数据挖掘，需求分析，商业数据分析，sps...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>钛氪新媒体科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        职位职责：\\n1、负责inews新闻大数据产品数据分析，提出数据问题、分...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>伊澳科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"股票期权\",\"绩效奖金\",\"年终分红\"]</td>\n",
       "      <td>\\n        职位描述\\n1.基于对业务深入理解的基础上，搭建业务分析模型，为业务提供...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数数科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>A轮</td>\n",
       "      <td>游戏</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"股票期权\",\"带薪年假\",\"年终分红\"]</td>\n",
       "      <td>\\n        职位描述：\\n\\n1. 负责数据后台的实施落地，引导客户了解产品使用方法...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>省钱快报</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>C轮</td>\n",
       "      <td>电商</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]</td>\n",
       "      <td>\\n        职位描述：\\n1、负责搜索和推荐业务的数据分析工作；\\n2、基于内外部的...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>省钱快报</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>C轮</td>\n",
       "      <td>电商</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]</td>\n",
       "      <td>\\n        职位描述： \\n1、负责搜索和推荐业务的数据分析工作 \\n2、基于内外部...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>蛋壳公寓</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"试用期上公积金\",\"地铁周边\",\"6险1金\"]</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"岗位晋升\",\"年度旅游\"]</td>\n",
       "      <td>\\n岗位职责：\\n1、深入洞察行业、用户、业务背景，分析核心数据，制定科学目标；\\n2、建立...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>钛氪新媒体科技</td>\n",
       "      <td>北京</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n\\n\\n\\n职位职责：\\n\\n1、负责inews新闻大数据产品的数据分析、挖掘、监控、整...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>小帮规划</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>金融</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"五险一金\",\"午餐补助\",\"交通补助\",\"带薪年假\"]</td>\n",
       "      <td>\\n        这个职位需要做什么？\\n1. 业务数据分析体系优化：包含但不限于业务数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>美图公司</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>硬件</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"与大牛共事\",\"福利健全\",\"五险一金\"]</td>\n",
       "      <td>\\n        岗位职责： \\n\\n1、关注市场动态，做行业深度分析，定期产出行业以及竞...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>魔力耳朵</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"弹性工作\",\"节日礼物\",\"领导好\"]</td>\n",
       "      <td>\\n        【岗位职责】\\n1.基于对教学服务深入理解的基础上，搭建业务分析模型，统...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>用友</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n数据分析师 岗位职责:1、 负责政府数据产品和项目中的数据治理、数据分析、数据展示工作；...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>跟谁学</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>教育</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>最右</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"连续创业团队\",\"年度旅游\",\"扁平管理\"]</td>\n",
       "      <td>\\n职责:\\n1.对最右产品运营数据进行分析、监测、统计，并形成指标体系；\\n2.制定业务核...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>数据分析师（教育产品）</td>\n",
       "      <td>跟谁学</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>教育</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>ZMT众盟数据</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>C轮</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"股票期权\",\"专项奖金\",\"绩效奖金\",\"五险一金\"]</td>\n",
       "      <td>\\n        岗位职责：\\n 1、负责每日部门日常数据报表与分析指标；\\n  2、负责...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>探探</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>社交</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"股票期权\",\"带薪年假\",\"岗位晋升\",\"扁平管理\"]</td>\n",
       "      <td>\\n职位职责：\\n1、整体负责探探下产品业务线的各项数据分析相关工作\\n2、协助构建新的数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>易观</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>37.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>[\"早十晚七\",\"生日聚会\"]</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]</td>\n",
       "      <td>\\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>易观</td>\n",
       "      <td>北京</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>37.5</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"早十晚七\",\"生日聚会\"]</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]</td>\n",
       "      <td>\\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>转转</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"定期体检\",\"交通补助\",\"年底双薪\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、基于运营数据、客户反馈和海量业务数据等进行分析和挖掘，...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>优信集团</td>\n",
       "      <td>北京</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>21.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"通讯津贴\",\"带薪年假\",\"交通补助\"]</td>\n",
       "      <td>\\n        工作职责：\\n1.基于对业务的理解，搭建可准确反映业务运作状况的数据指标...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>G7</td>\n",
       "      <td>北京</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>物联网</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"专项奖金\",\"绩效奖金\"]</td>\n",
       "      <td>\\n        工作职责：\\n1.对日常经营和平台数据进行采集和加工，通过数据洞察业务经...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>2822</td>\n",
       "      <td>2992</td>\n",
       "      <td>大数据分析岗</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>厦门</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>26.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"带薪年假\",\"交通补助\",\"通讯津贴\"]</td>\n",
       "      <td>\\n        根据不同业务场景，应用数据挖掘、机器学习等方法，开展多领域的数据分析和建...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>2876</td>\n",
       "      <td>3047</td>\n",
       "      <td>手游数据分析师</td>\n",
       "      <td>草花互动</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>游戏</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"股票期权\",\"五险一金\",\"年终分红\"]</td>\n",
       "      <td>\\n岗位职责：\\n1、构建并完善游戏项目运营数据分析体系，提供日常游戏业务相关的数据支持；\\...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>2878</td>\n",
       "      <td>3049</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>绝味食品</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责品牌中心日常管理数据清洗与维护；\\n2、负责按实际...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>2879</td>\n",
       "      <td>3050</td>\n",
       "      <td>数据产品经理（数据分析方向）</td>\n",
       "      <td>纬度科技</td>\n",
       "      <td>长沙</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>教育</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"带薪年假\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1. 负责公司数据产品的需求管理、产品研发、横向纵向沟通、...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>2880</td>\n",
       "      <td>3051</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>58到家</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>[\"试用期上社保\",\"试用期上公积金\",\"免费健身房\",\"免费下午茶\",\"公司氛围好\",\"1...</td>\n",
       "      <td>[\"节日礼物\",\"技能培训\",\"股票期权\",\"免费班车\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1，负责家政业务的数据整合，数据指标体系，数据标签体系，数...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>2883</td>\n",
       "      <td>3054</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>宇信科技集团</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>金融</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1. 围绕银行业务目标进行数据分析、模型设计。\\n2. 业...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>2884</td>\n",
       "      <td>3055</td>\n",
       "      <td>数据分析师（食宿+五险）</td>\n",
       "      <td>潭州教育</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>B轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1.负责部门推广运营及多渠道业务数据的整理和分析，制作数据...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>2886</td>\n",
       "      <td>3057</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数魔</td>\n",
       "      <td>长沙</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>电商</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"带薪年假\",\"年度旅游\",\"领导好\"]</td>\n",
       "      <td>\\n        位职责：\\n1、完成因项目需要而开展的数据处理、整合，参与算法模型的开发...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>2887</td>\n",
       "      <td>3058</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>益丰大药房</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>电商</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"绩效奖金\",\"五险一金\",\"免费午餐\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、提取商品销售等数据，进行数据分析，制作数据报表及数据可...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>2888</td>\n",
       "      <td>3059</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>南京汇龙</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n数据岗位要求：（工作地点：长沙 ）\\n工作要求\\n1、电信移动用户维系相关数据统计及分析...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>2889</td>\n",
       "      <td>3060</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>233网校</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>教育</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"节日礼物\",\"年底双薪\",\"技能培训\",\"岗位晋升\"]</td>\n",
       "      <td>\\n岗位职责： 1、优化运营数据分析体系，帮助确定各项运营数据指标； 2、配合业务部门给予数...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>2890</td>\n",
       "      <td>3061</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京天意飞扬科技</td>\n",
       "      <td>长沙</td>\n",
       "      <td>少于15人</td>\n",
       "      <td>不限</td>\n",
       "      <td>未融资</td>\n",
       "      <td>电商</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n1、能熟练使用sql分析数据，熟练使用数理统计、数据分析、数据挖掘工具软件（sas、r、...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>2891</td>\n",
       "      <td>3062</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>蜜獾律师事务所</td>\n",
       "      <td>长沙</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责： 1.负责业务相关数据的获取、清洗、分析、挖掘和可视化，从数据收集/准备到模型...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>2892</td>\n",
       "      <td>3063</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>蜜獾信息</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n岗位职责：\\n1． 熟悉公司产品，对公司产品相关的业务数据进行分析、整理，产出基于数据的...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>2893</td>\n",
       "      <td>3064</td>\n",
       "      <td>bi数据分析师</td>\n",
       "      <td>武汉盛博汇</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>大专</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>医疗丨健康</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、正确理解业务，完成数据仓库主题设计，核心表设计，各类源...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>2894</td>\n",
       "      <td>3065</td>\n",
       "      <td>数据分析师中级（SC0902）</td>\n",
       "      <td>蜜獾信息</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]</td>\n",
       "      <td>\\n工作职责:1.与中国及美国总部的it部门，和业务部门紧密协作，搜集专业的房产及交易市场信...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>2895</td>\n",
       "      <td>3066</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>政法频道</td>\n",
       "      <td>长沙</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>22.5</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责：\\n负责新闻资讯、用户行为数据等海量数据采集、处理、存储，应用方案的技术选型及...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>3164</td>\n",
       "      <td>3339</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>中地行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>[\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]</td>\n",
       "      <td>[\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]</td>\n",
       "      <td>\\n岗位描述：1、负责银行数据分析模型的建立、调优和迭代；2、负责银行数据的清洗加工；3、负...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>3165</td>\n",
       "      <td>3340</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>神彩科技</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"带薪年假\",\"管吃\",\"领导好\",\"全额五险一金\"]</td>\n",
       "      <td>\\n岗位要求：\\n本科及以上学历，计算机、数学、统计学；\\n1.具有丰富的sql经验；\\n2...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>3166</td>\n",
       "      <td>3341</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>同程旅行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>旅游</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"股票期权\",\"年底双薪\",\"五险一金\"]</td>\n",
       "      <td>\\n工作职责：\\n1、本地生活业务数据分析，订单、营收、用户分级、留存、运营等策略方向分析；...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>3167</td>\n",
       "      <td>3342</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>K2DATA</td>\n",
       "      <td>苏州</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>本科</td>\n",
       "      <td>B轮</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"风口行业\",\"顶尖团队\",\"扁平管理\",\"学习型组织\"]</td>\n",
       "      <td>\\n岗位说明：\\n1. 针对重点工业行业（能源，石化，汽车，钢铁、轮胎等）的智能制造与工业互...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>3170</td>\n",
       "      <td>3345</td>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>昆山农商银行</td>\n",
       "      <td>苏州</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"专项奖金\",\"午餐补助\",\"节日礼物\",\"技能培训\"]</td>\n",
       "      <td>\\n        工作职责:\\n1、根据业务进行数据集市与主维度模型的设计与建设，规范数据...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>3171</td>\n",
       "      <td>3346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>苏州瑞鹏信息技术有限公司</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>硕士</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        1、日常数据监控，包括日报设计、开发、维护等，完善数据报表体系，及时准确...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>3174</td>\n",
       "      <td>3349</td>\n",
       "      <td>游戏数据分析师</td>\n",
       "      <td>友谊时光</td>\n",
       "      <td>苏州</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>本科</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>游戏</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]</td>\n",
       "      <td>\\n        岗位职责：\\n1、协助产品监控和跟踪游戏数据，出具敏捷报告；\\n2、对游...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>3176</td>\n",
       "      <td>3351</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>瑞小云</td>\n",
       "      <td>苏州</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"绩效奖金\",\"专项奖金\",\"股票期权\",\"年度旅游\"]</td>\n",
       "      <td>\\n职位描述1.用户运营数据分析支撑，通过用户特征、行为数据、产品转化数据等对业务进行挖掘和...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>3177</td>\n",
       "      <td>3352</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>华泛信息</td>\n",
       "      <td>苏州</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>大专</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>其他</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"节日礼物\",\"带薪年假\",\"绩效奖金\"]</td>\n",
       "      <td>\\n岗位职责：\\n深入了解项目组的业务需求,在此基础上进行数据收集、数据分析、商业报告的撰写...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>3178</td>\n",
       "      <td>3353</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>德融嘉信</td>\n",
       "      <td>苏州</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>本科</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n岗位职责：\\n1. 开展业务专题分析，使用数据挖掘各类算法构建相关的业务模型，完成业务分...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>3404</td>\n",
       "      <td>3580</td>\n",
       "      <td>ETL/大数据/数据分析/实施</td>\n",
       "      <td>格蒂电力</td>\n",
       "      <td>天津</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>大专</td>\n",
       "      <td>未融资</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"技能培训\",\"带薪年假\",\"绩效奖金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n工作职责\\n1.   负责数据接入、数据整合中的链路配置与调度配置工作。\\n职位要求\\n...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>3405</td>\n",
       "      <td>3581</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>吉城美家</td>\n",
       "      <td>天津</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"五险一金\",\"岗位晋升\"]</td>\n",
       "      <td>\\n负责站点日常数据分析、提前通过数据分析对业务有预测性、通过数据说话、解决站点管理问题、\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>3406</td>\n",
       "      <td>3582</td>\n",
       "      <td>数据分析支持</td>\n",
       "      <td>云链供应链</td>\n",
       "      <td>天津</td>\n",
       "      <td>15-50人</td>\n",
       "      <td>本科</td>\n",
       "      <td>未融资</td>\n",
       "      <td>金融</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\n        岗位职责：\\n1、负责部门日常数据报表的制定、维护、优化；\\n2、支持运...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1322 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      level_0  index     positionName companyShortName city companySize  \\\n",
       "0           0      0          高级数据分析师              拉勾网   北京   500-2000人   \n",
       "1           1      1            数据分析师         OK Group   北京   500-2000人   \n",
       "2           2      2          高级数据分析师           金山办公软件   北京     2000人以上   \n",
       "3           3      3            数据分析师           金山办公软件   北京     2000人以上   \n",
       "4           4      4            数据分析师             京东集团   北京     2000人以上   \n",
       "5           5      5            数据分析师           鲸鱼外教培优   北京   500-2000人   \n",
       "6           6      6             数据分析             北银消费   北京    150-500人   \n",
       "7           7      7            数据分析师   时趣social-touch   北京   500-2000人   \n",
       "8           8      8            数据分析师             e城e家   北京     2000人以上   \n",
       "9           9      9           数据分析专员          钛氪新媒体科技   北京     50-150人   \n",
       "10         10     10           数据分析专家             伊澳科技   北京     2000人以上   \n",
       "11         11     11            数据分析师             数数科技   北京      15-50人   \n",
       "12         12     12            数据分析师             省钱快报   北京    150-500人   \n",
       "13         13     13             数据分析             省钱快报   北京    150-500人   \n",
       "14         14     14            数据分析师             蛋壳公寓   北京     2000人以上   \n",
       "15         15     16           数据分析专员          钛氪新媒体科技   北京     50-150人   \n",
       "16         16     17            数据分析师             小帮规划   北京   500-2000人   \n",
       "17         17     18            数据分析师             美图公司   北京     2000人以上   \n",
       "18         18     21            数据分析师             魔力耳朵   北京   500-2000人   \n",
       "19         19     22          高级数据分析师               用友   北京   500-2000人   \n",
       "20         20     23            数据分析师              跟谁学   北京     2000人以上   \n",
       "21         21     24             数据分析               最右   北京   500-2000人   \n",
       "23         23     26      数据分析师（教育产品）              跟谁学   北京     2000人以上   \n",
       "24         24     27            数据分析师          ZMT众盟数据   北京   500-2000人   \n",
       "25         25     29          高级数据分析师               探探   北京    150-500人   \n",
       "28         28     32            数据分析师               易观   北京    150-500人   \n",
       "29         29     33          高级数据分析师               易观   北京    150-500人   \n",
       "30         30     34            数据分析师               转转   北京   500-2000人   \n",
       "31         31     35            数据分析师             优信集团   北京     2000人以上   \n",
       "32         32     36            数据分析师               G7   北京   500-2000人   \n",
       "...       ...    ...              ...              ...  ...         ...   \n",
       "1613     2822   2992           大数据分析岗             建信金科   厦门     2000人以上   \n",
       "1614     2876   3047          手游数据分析师             草花互动   长沙    150-500人   \n",
       "1616     2878   3049            数据分析师             绝味食品   长沙     2000人以上   \n",
       "1617     2879   3050   数据产品经理（数据分析方向）             纬度科技   长沙      15-50人   \n",
       "1618     2880   3051            数据分析师             58到家   长沙     2000人以上   \n",
       "1621     2883   3054            数据分析师           宇信科技集团   长沙     2000人以上   \n",
       "1622     2884   3055     数据分析师（食宿+五险）             潭州教育   长沙     2000人以上   \n",
       "1624     2886   3057            数据分析师               数魔   长沙     50-150人   \n",
       "1625     2887   3058           数据分析主管            益丰大药房   长沙     2000人以上   \n",
       "1626     2888   3059             数据分析             南京汇龙   长沙    150-500人   \n",
       "1627     2889   3060            数据分析师            233网校   长沙    150-500人   \n",
       "1628     2890   3061             数据分析         北京天意飞扬科技   长沙       少于15人   \n",
       "1629     2891   3062             数据分析          蜜獾律师事务所   长沙     50-150人   \n",
       "1630     2892   3063            数据分析师             蜜獾信息   长沙    150-500人   \n",
       "1631     2893   3064          bi数据分析师            武汉盛博汇   长沙    150-500人   \n",
       "1632     2894   3065  数据分析师中级（SC0902）             蜜獾信息   长沙    150-500人   \n",
       "1633     2895   3066           大数据分析师             政法频道   长沙    150-500人   \n",
       "1634     3164   3339          数据分析工程师              中地行   苏州     50-150人   \n",
       "1635     3165   3340          数据分析工程师             神彩科技   苏州    150-500人   \n",
       "1636     3166   3341            数据分析师             同程旅行   苏州     2000人以上   \n",
       "1637     3167   3342          高级数据分析师           K2DATA   苏州    150-500人   \n",
       "1640     3170   3345            数据分析岗           昆山农商银行   苏州   500-2000人   \n",
       "1641     3171   3346            数据分析师     苏州瑞鹏信息技术有限公司   苏州     50-150人   \n",
       "1644     3174   3349          游戏数据分析师             友谊时光   苏州   500-2000人   \n",
       "1646     3176   3351            数据分析师              瑞小云   苏州      15-50人   \n",
       "1647     3177   3352            数据分析师             华泛信息   苏州     2000人以上   \n",
       "1648     3178   3353           大数据分析师             德融嘉信   苏州     50-150人   \n",
       "1649     3404   3580  ETL/大数据/数据分析/实施             格蒂电力   天津   500-2000人   \n",
       "1650     3405   3581            数据分析师             吉城美家   天津     2000人以上   \n",
       "1651     3406   3582           数据分析支持            云链供应链   天津      15-50人   \n",
       "\n",
       "     education financeStage industryField  salary workYear  \\\n",
       "0           本科        D轮及以上          企业服务    30.0    5-10年   \n",
       "1           大专           B轮            金融    35.0    5-10年   \n",
       "2           本科         上市公司         移动互联网    20.0     3-5年   \n",
       "3           本科         上市公司         移动互联网    20.0     1-3年   \n",
       "4           本科         上市公司            电商    22.5     3-5年   \n",
       "5           本科           B轮            教育    22.5     3-5年   \n",
       "6           本科        不需要融资            金融    17.5     1-3年   \n",
       "7           本科        D轮及以上         移动互联网    17.5     3-5年   \n",
       "8           本科        不需要融资          消费生活    20.0     3-5年   \n",
       "9           本科          未融资         移动互联网    12.0     1-3年   \n",
       "10          本科         上市公司            电商    45.0    5-10年   \n",
       "11          本科           A轮            游戏    15.0     1-3年   \n",
       "12          本科           C轮            电商    30.0     3-5年   \n",
       "13          本科           C轮            电商    30.0     3-5年   \n",
       "14          本科         上市公司          消费生活    22.5     3-5年   \n",
       "15          本科          未融资         移动互联网    12.0     1-3年   \n",
       "16          本科           B轮            金融    35.0     3-5年   \n",
       "17          本科         上市公司            硬件    35.0     3-5年   \n",
       "18          本科           B轮            教育    11.5     1-3年   \n",
       "19          本科         上市公司          数据服务    24.0     3-5年   \n",
       "20          本科         上市公司            教育    20.0     3-5年   \n",
       "21          本科        D轮及以上         文娱丨内容    22.5     1-3年   \n",
       "23          本科         上市公司            教育    22.5     3-5年   \n",
       "24          大专           C轮          数据服务    13.5     1-3年   \n",
       "25          本科        D轮及以上            社交    35.0     3-5年   \n",
       "28          本科           B轮          数据服务    37.5     3-5年   \n",
       "29          本科           B轮          数据服务    37.5    5-10年   \n",
       "30          本科           B轮          消费生活    22.5     1-3年   \n",
       "31          本科        D轮及以上         汽车丨出行    21.5     3-5年   \n",
       "32          本科        D轮及以上           物联网    20.0     3-5年   \n",
       "...        ...          ...           ...     ...      ...   \n",
       "1613        本科        不需要融资            金融    26.5     3-5年   \n",
       "1614        本科        不需要融资            游戏    11.5     1-3年   \n",
       "1616        本科         上市公司            电商    10.5     3-5年   \n",
       "1617        硕士        不需要融资            教育    15.0     1-3年   \n",
       "1618        本科        D轮及以上          消费生活    15.0    5-10年   \n",
       "1621        本科         上市公司            金融    13.0     1-3年   \n",
       "1622        大专           B轮            教育     4.5     1-3年   \n",
       "1624        本科          天使轮            电商    15.0     3-5年   \n",
       "1625        大专         上市公司            电商     7.5     3-5年   \n",
       "1626        本科          未融资         移动互联网     6.0     1-3年   \n",
       "1627        本科          天使轮            教育     9.0     3-5年   \n",
       "1628        不限          未融资            电商    10.0     1-3年   \n",
       "1629        本科          未融资            其他    10.5     3-5年   \n",
       "1630        本科        不需要融资         移动互联网     6.0     1-3年   \n",
       "1631        大专        不需要融资         医疗丨健康     8.5     1-3年   \n",
       "1632        本科        不需要融资         移动互联网     8.0     1-3年   \n",
       "1633        硕士        不需要融资            其他    22.5    5-10年   \n",
       "1634        本科        不需要融资          数据服务    11.5     1-3年   \n",
       "1635        本科        不需要融资          数据服务    10.0     1-3年   \n",
       "1636        本科         上市公司            旅游    22.5     3-5年   \n",
       "1637        本科           B轮          数据服务    22.5     3-5年   \n",
       "1640        本科          未融资            金融    18.0     1-3年   \n",
       "1641        硕士        不需要融资          数据服务    18.0     1-3年   \n",
       "1644        本科         上市公司            游戏    11.5     1-3年   \n",
       "1646        本科        不需要融资         移动互联网    15.0     3-5年   \n",
       "1647        大专        不需要融资            其他    12.5     1-3年   \n",
       "1648        本科        不需要融资         移动互联网     9.0     1-3年   \n",
       "1649        大专          未融资          企业服务     9.0     3-5年   \n",
       "1650        本科          未融资         移动互联网    10.5     1-3年   \n",
       "1651        本科          未融资            金融     5.0     1-3年   \n",
       "\n",
       "                                                 hitags  \\\n",
       "0     [\"免费下午茶\",\"ipo倒计时\",\"bat背景\",\"地铁周边\",\"每天管两餐\",\"定期团建...   \n",
       "1                                                   NaN   \n",
       "2                                                   NaN   \n",
       "3                                                   NaN   \n",
       "4                                [\"免费班车\",\"免费体检\",\"地铁周边\"]   \n",
       "5                                                   NaN   \n",
       "6                                                   NaN   \n",
       "7                                                   NaN   \n",
       "8                                                   NaN   \n",
       "9                                                   NaN   \n",
       "10                                                  NaN   \n",
       "11                                                  NaN   \n",
       "12                                                  NaN   \n",
       "13                                                  NaN   \n",
       "14                            [\"试用期上公积金\",\"地铁周边\",\"6险1金\"]   \n",
       "15                                                  NaN   \n",
       "16                                                  NaN   \n",
       "17                                                  NaN   \n",
       "18                                                  NaN   \n",
       "19                                                  NaN   \n",
       "20                                                  NaN   \n",
       "21                                                  NaN   \n",
       "23                                                  NaN   \n",
       "24                                                  NaN   \n",
       "25                                                  NaN   \n",
       "28                                      [\"早十晚七\",\"生日聚会\"]   \n",
       "29                                      [\"早十晚七\",\"生日聚会\"]   \n",
       "30                                                  NaN   \n",
       "31                                                  NaN   \n",
       "32                                                  NaN   \n",
       "...                                                 ...   \n",
       "1613                                                NaN   \n",
       "1614                                                NaN   \n",
       "1616                                                NaN   \n",
       "1617                                                NaN   \n",
       "1618  [\"试用期上社保\",\"试用期上公积金\",\"免费健身房\",\"免费下午茶\",\"公司氛围好\",\"1...   \n",
       "1621                                                NaN   \n",
       "1622                                                NaN   \n",
       "1624                                                NaN   \n",
       "1625                                                NaN   \n",
       "1626                                                NaN   \n",
       "1627                                                NaN   \n",
       "1628                                                NaN   \n",
       "1629                                                NaN   \n",
       "1630                                                NaN   \n",
       "1631                                                NaN   \n",
       "1632                                                NaN   \n",
       "1633                                                NaN   \n",
       "1634                     [\"免费下午茶\",\"免费体检\",\"5险1金\",\"地铁周边\"]   \n",
       "1635                                                NaN   \n",
       "1636                                                NaN   \n",
       "1637                                                NaN   \n",
       "1640                                                NaN   \n",
       "1641                                                NaN   \n",
       "1644                                                NaN   \n",
       "1646                                                NaN   \n",
       "1647                                                NaN   \n",
       "1648                                                NaN   \n",
       "1649                                                NaN   \n",
       "1650                                                NaN   \n",
       "1651                                                NaN   \n",
       "\n",
       "                     companyLabelList  \\\n",
       "0       [\"五险一金\",\"弹性工作\",\"带薪年假\",\"免费两餐\"]   \n",
       "1        [\"节日礼物\",\"年度旅游\",\"扁平管理\",\"领导好\"]   \n",
       "2       [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "3       [\"年底双薪\",\"节日礼物\",\"技能培训\",\"绩效奖金\"]   \n",
       "4       [\"五险一金\",\"带薪年假\",\"免费班车\",\"定期体检\"]   \n",
       "5       [\"交通补助\",\"绩效奖金\",\"带薪年假\",\"节日礼物\"]   \n",
       "6       [\"节日礼物\",\"技能培训\",\"绩效奖金\",\"岗位晋升\"]   \n",
       "7       [\"弹性工作\",\"年终奖金\",\"个性福利\",\"带薪年假\"]   \n",
       "8       [\"年底双薪\",\"绩效奖金\",\"带薪年假\",\"午餐补助\"]   \n",
       "9                                  []   \n",
       "10      [\"专项奖金\",\"股票期权\",\"绩效奖金\",\"年终分红\"]   \n",
       "11      [\"节日礼物\",\"股票期权\",\"带薪年假\",\"年终分红\"]   \n",
       "12      [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]   \n",
       "13      [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"股票期权\"]   \n",
       "14      [\"技能培训\",\"带薪年假\",\"岗位晋升\",\"年度旅游\"]   \n",
       "15                                 []   \n",
       "16      [\"五险一金\",\"午餐补助\",\"交通补助\",\"带薪年假\"]   \n",
       "17     [\"节日礼物\",\"与大牛共事\",\"福利健全\",\"五险一金\"]   \n",
       "18       [\"带薪年假\",\"弹性工作\",\"节日礼物\",\"领导好\"]   \n",
       "19                                 []   \n",
       "20      [\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]   \n",
       "21    [\"技能培训\",\"连续创业团队\",\"年度旅游\",\"扁平管理\"]   \n",
       "23      [\"节日礼物\",\"技能培训\",\"股票期权\",\"精英团队\"]   \n",
       "24      [\"股票期权\",\"专项奖金\",\"绩效奖金\",\"五险一金\"]   \n",
       "25      [\"股票期权\",\"带薪年假\",\"岗位晋升\",\"扁平管理\"]   \n",
       "28      [\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]   \n",
       "29      [\"专项奖金\",\"午餐补助\",\"技能培训\",\"扁平管理\"]   \n",
       "30      [\"带薪年假\",\"定期体检\",\"交通补助\",\"年底双薪\"]   \n",
       "31      [\"绩效奖金\",\"通讯津贴\",\"带薪年假\",\"交通补助\"]   \n",
       "32      [\"节日礼物\",\"技能培训\",\"专项奖金\",\"绩效奖金\"]   \n",
       "...                               ...   \n",
       "1613    [\"绩效奖金\",\"带薪年假\",\"交通补助\",\"通讯津贴\"]   \n",
       "1614    [\"专项奖金\",\"股票期权\",\"五险一金\",\"年终分红\"]   \n",
       "1616                               []   \n",
       "1617    [\"年底双薪\",\"带薪年假\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1618    [\"节日礼物\",\"技能培训\",\"股票期权\",\"免费班车\"]   \n",
       "1621                               []   \n",
       "1622                               []   \n",
       "1624     [\"年底双薪\",\"带薪年假\",\"年度旅游\",\"领导好\"]   \n",
       "1625    [\"年底双薪\",\"绩效奖金\",\"五险一金\",\"免费午餐\"]   \n",
       "1626                               []   \n",
       "1627    [\"节日礼物\",\"年底双薪\",\"技能培训\",\"岗位晋升\"]   \n",
       "1628                               []   \n",
       "1629                               []   \n",
       "1630    [\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1631                               []   \n",
       "1632    [\"年底双薪\",\"技能培训\",\"绩效奖金\",\"年度旅游\"]   \n",
       "1633                               []   \n",
       "1634    [\"带薪年假\",\"定期体检\",\"专项奖金\",\"年底双薪\"]   \n",
       "1635     [\"带薪年假\",\"管吃\",\"领导好\",\"全额五险一金\"]   \n",
       "1636    [\"绩效奖金\",\"股票期权\",\"年底双薪\",\"五险一金\"]   \n",
       "1637   [\"风口行业\",\"顶尖团队\",\"扁平管理\",\"学习型组织\"]   \n",
       "1640    [\"专项奖金\",\"午餐补助\",\"节日礼物\",\"技能培训\"]   \n",
       "1641                               []   \n",
       "1644    [\"技能培训\",\"年底双薪\",\"节日礼物\",\"绩效奖金\"]   \n",
       "1646    [\"绩效奖金\",\"专项奖金\",\"股票期权\",\"年度旅游\"]   \n",
       "1647    [\"技能培训\",\"节日礼物\",\"带薪年假\",\"绩效奖金\"]   \n",
       "1648                               []   \n",
       "1649    [\"技能培训\",\"带薪年假\",\"绩效奖金\",\"岗位晋升\"]   \n",
       "1650                  [\"五险一金\",\"岗位晋升\"]   \n",
       "1651                               []   \n",
       "\n",
       "                                             job_detail  Python  SQL  Tableau  \\\n",
       "0     \\n1.搭建数据指标框架，完整并准确反映业务趋势和变化，及时发现和定位问题\\n2.独立完成数...       1    1        0   \n",
       "1     \\n工作职责：\\n1. 负责建立交易平台日常分析体系，包括核心指标体系、报表体系，专题活动分...       0    1        0   \n",
       "2     \\n职位描述：1.对亿计的办公用户数据进行深度挖掘，引导产品、运营，并能实际应用到业务中带来...       1    1        0   \n",
       "3     \\n工作职责：-负责日常运营、业务数据等分析-针对产品需求做深入的数据分析报告，分析用户行为...       0    1        0   \n",
       "4     \\n【数据分析师岗】\\n岗位要求：\\n1、构建及维护客户体验相关数据报表平台；\\n2、与大数...       0    1        1   \n",
       "5     \\n        1.负责业务数据分析工作，基于业务场景，建立核心业务数据模型及预警体系；...       0    0        0   \n",
       "6     \\n        职责描述：\\n1、对公司业务数据进行分析，提出有效合理的风控建议；\\n2...       0    0        0   \n",
       "7     \\n        岗位描述：\\n品牌舆情，社交分析，服务过品牌客户，发布周期性评估报告（美...       0    1        0   \n",
       "8     \\n        技能要求：\\n数据分析，sql，数据挖掘，需求分析，商业数据分析，sps...       1    1        0   \n",
       "9     \\n        职位职责：\\n1、负责inews新闻大数据产品数据分析，提出数据问题、分...       1    1        0   \n",
       "10    \\n        职位描述\\n1.基于对业务深入理解的基础上，搭建业务分析模型，为业务提供...       0    0        0   \n",
       "11    \\n        职位描述：\\n\\n1. 负责数据后台的实施落地，引导客户了解产品使用方法...       0    0        0   \n",
       "12    \\n        职位描述：\\n1、负责搜索和推荐业务的数据分析工作；\\n2、基于内外部的...       1    1        0   \n",
       "13    \\n        职位描述： \\n1、负责搜索和推荐业务的数据分析工作 \\n2、基于内外部...       1    1        0   \n",
       "14    \\n岗位职责：\\n1、深入洞察行业、用户、业务背景，分析核心数据，制定科学目标；\\n2、建立...       1    1        0   \n",
       "15    \\n\\n\\n\\n职位职责：\\n\\n1、负责inews新闻大数据产品的数据分析、挖掘、监控、整...       1    1        0   \n",
       "16    \\n        这个职位需要做什么？\\n1. 业务数据分析体系优化：包含但不限于业务数据...       0    0        0   \n",
       "17    \\n        岗位职责： \\n\\n1、关注市场动态，做行业深度分析，定期产出行业以及竞...       0    0        0   \n",
       "18    \\n        【岗位职责】\\n1.基于对教学服务深入理解的基础上，搭建业务分析模型，统...       0    0        0   \n",
       "19    \\n数据分析师 岗位职责:1、 负责政府数据产品和项目中的数据治理、数据分析、数据展示工作；...       1    0        0   \n",
       "20    \\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...       0    1        0   \n",
       "21    \\n职责:\\n1.对最右产品运营数据进行分析、监测、统计，并形成指标体系；\\n2.制定业务核...       1    1        0   \n",
       "23    \\n        岗位职责：\\n1、 深度了解公司业务，通过业务数据表现发掘用户、运营、人...       0    1        0   \n",
       "24    \\n        岗位职责：\\n 1、负责每日部门日常数据报表与分析指标；\\n  2、负责...       0    1        0   \n",
       "25    \\n职位职责：\\n1、整体负责探探下产品业务线的各项数据分析相关工作\\n2、协助构建新的数据...       0    1        1   \n",
       "28    \\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...       0    1        0   \n",
       "29    \\n岗位职责：\\n1.深入了解互联网业务，建立基于业务场景的数据分析需求，解决各类数据分析问...       0    1        0   \n",
       "30    \\n        岗位职责：\\n1、基于运营数据、客户反馈和海量业务数据等进行分析和挖掘，...       1    1        1   \n",
       "31    \\n        工作职责：\\n1.基于对业务的理解，搭建可准确反映业务运作状况的数据指标...       0    1        0   \n",
       "32    \\n        工作职责：\\n1.对日常经营和平台数据进行采集和加工，通过数据洞察业务经...       0    0        1   \n",
       "...                                                 ...     ...  ...      ...   \n",
       "1613  \\n        根据不同业务场景，应用数据挖掘、机器学习等方法，开展多领域的数据分析和建...       1    1        0   \n",
       "1614  \\n岗位职责：\\n1、构建并完善游戏项目运营数据分析体系，提供日常游戏业务相关的数据支持；\\...       0    0        0   \n",
       "1616  \\n        岗位职责：\\n1、负责品牌中心日常管理数据清洗与维护；\\n2、负责按实际...       0    1        1   \n",
       "1617  \\n        岗位职责：\\n1. 负责公司数据产品的需求管理、产品研发、横向纵向沟通、...       1    1        0   \n",
       "1618  \\n        岗位职责：\\n1，负责家政业务的数据整合，数据指标体系，数据标签体系，数...       1    0        0   \n",
       "1621  \\n        岗位职责：\\n1. 围绕银行业务目标进行数据分析、模型设计。\\n2. 业...       1    1        0   \n",
       "1622  \\n        岗位职责：\\n1.负责部门推广运营及多渠道业务数据的整理和分析，制作数据...       0    0        0   \n",
       "1624  \\n        位职责：\\n1、完成因项目需要而开展的数据处理、整合，参与算法模型的开发...       1    1        1   \n",
       "1625  \\n        岗位职责：\\n1、提取商品销售等数据，进行数据分析，制作数据报表及数据可...       0    0        0   \n",
       "1626  \\n数据岗位要求：（工作地点：长沙 ）\\n工作要求\\n1、电信移动用户维系相关数据统计及分析...       1    1        1   \n",
       "1627  \\n岗位职责： 1、优化运营数据分析体系，帮助确定各项运营数据指标； 2、配合业务部门给予数...       1    1        0   \n",
       "1628  \\n1、能熟练使用sql分析数据，熟练使用数理统计、数据分析、数据挖掘工具软件（sas、r、...       1    1        1   \n",
       "1629  \\n岗位职责： 1.负责业务相关数据的获取、清洗、分析、挖掘和可视化，从数据收集/准备到模型...       1    1        0   \n",
       "1630  \\n岗位职责：\\n1． 熟悉公司产品，对公司产品相关的业务数据进行分析、整理，产出基于数据的...       0    1        0   \n",
       "1631  \\n        岗位职责：\\n1、正确理解业务，完成数据仓库主题设计，核心表设计，各类源...       0    1        0   \n",
       "1632  \\n工作职责:1.与中国及美国总部的it部门，和业务部门紧密协作，搜集专业的房产及交易市场信...       0    1        0   \n",
       "1633  \\n岗位职责：\\n负责新闻资讯、用户行为数据等海量数据采集、处理、存储，应用方案的技术选型及...       1    0        0   \n",
       "1634  \\n岗位描述：1、负责银行数据分析模型的建立、调优和迭代；2、负责银行数据的清洗加工；3、负...       1    1        0   \n",
       "1635  \\n岗位要求：\\n本科及以上学历，计算机、数学、统计学；\\n1.具有丰富的sql经验；\\n2...       1    1        1   \n",
       "1636  \\n工作职责：\\n1、本地生活业务数据分析，订单、营收、用户分级、留存、运营等策略方向分析；...       0    1        0   \n",
       "1637  \\n岗位说明：\\n1. 针对重点工业行业（能源，石化，汽车，钢铁、轮胎等）的智能制造与工业互...       1    0        0   \n",
       "1640  \\n        工作职责:\\n1、根据业务进行数据集市与主维度模型的设计与建设，规范数据...       1    1        0   \n",
       "1641  \\n        1、日常数据监控，包括日报设计、开发、维护等，完善数据报表体系，及时准确...       1    1        0   \n",
       "1644  \\n        岗位职责：\\n1、协助产品监控和跟踪游戏数据，出具敏捷报告；\\n2、对游...       1    1        0   \n",
       "1646  \\n职位描述1.用户运营数据分析支撑，通过用户特征、行为数据、产品转化数据等对业务进行挖掘和...       1    1        0   \n",
       "1647  \\n岗位职责：\\n深入了解项目组的业务需求,在此基础上进行数据收集、数据分析、商业报告的撰写...       1    1        0   \n",
       "1648  \\n岗位职责：\\n1. 开展业务专题分析，使用数据挖掘各类算法构建相关的业务模型，完成业务分...       1    1        0   \n",
       "1649  \\n工作职责\\n1.   负责数据接入、数据整合中的链路配置与调度配置工作。\\n职位要求\\n...       0    1        0   \n",
       "1650    \\n负责站点日常数据分析、提前通过数据分析对业务有预测性、通过数据说话、解决站点管理问题、\\n       0    0        0   \n",
       "1651  \\n        岗位职责：\\n1、负责部门日常数据报表的制定、维护、优化；\\n2、支持运...       0    0        0   \n",
       "\n",
       "      Excel  SPSS/SAS  workYearNum  \n",
       "0         0         0            5  \n",
       "1         0         0            5  \n",
       "2         0         0            4  \n",
       "3         0         1            3  \n",
       "4         1         1            4  \n",
       "5         0         0            4  \n",
       "6         1         1            3  \n",
       "7         1         0            4  \n",
       "8         1         1            4  \n",
       "9         1         0            3  \n",
       "10        0         0            5  \n",
       "11        0         0            3  \n",
       "12        0         0            4  \n",
       "13        0         0            4  \n",
       "14        1         0            4  \n",
       "15        1         0            3  \n",
       "16        0         0            4  \n",
       "17        0         0            4  \n",
       "18        0         1            3  \n",
       "19        1         0            4  \n",
       "20        1         0            4  \n",
       "21        1         0            3  \n",
       "23        1         0            4  \n",
       "24        1         0            3  \n",
       "25        0         0            4  \n",
       "28        0         0            4  \n",
       "29        0         0            5  \n",
       "30        1         1            3  \n",
       "31        0         0            4  \n",
       "32        1         0            4  \n",
       "...     ...       ...          ...  \n",
       "1613      0         1            4  \n",
       "1614      0         0            3  \n",
       "1616      1         1            4  \n",
       "1617      1         0            3  \n",
       "1618      0         0            5  \n",
       "1621      0         0            3  \n",
       "1622      1         0            3  \n",
       "1624      1         0            4  \n",
       "1625      0         0            4  \n",
       "1626      0         1            3  \n",
       "1627      0         1            4  \n",
       "1628      0         1            3  \n",
       "1629      0         0            4  \n",
       "1630      1         0            3  \n",
       "1631      0         0            3  \n",
       "1632      0         0            3  \n",
       "1633      0         0            5  \n",
       "1634      0         1            3  \n",
       "1635      1         1            3  \n",
       "1636      1         0            4  \n",
       "1637      0         0            4  \n",
       "1640      0         0            3  \n",
       "1641      0         1            3  \n",
       "1644      0         0            3  \n",
       "1646      0         0            4  \n",
       "1647      1         0            3  \n",
       "1648      1         1            3  \n",
       "1649      0         0            4  \n",
       "1650      0         0            3  \n",
       "1651      0         0            3  \n",
       "\n",
       "[1322 rows x 20 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "job2['workYearNum'] = job2['workYear'].map(workYear_map)\n",
    "job2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "sized-debate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "\n",
    "#将str类型转换成类别类型，特殊的数据类型，再进行排序\n",
    "job2['companySize'] = job2['companySize'].astype('category')\n",
    "job2.dtypes\n",
    "#顺序\n",
    "list_num = ['2000人以上','500-2000人','150-500人','50-150人','15-50人','少于15人']\n",
    "#reorder 重新排序\n",
    "job2.companySize.cat.reorder_categories(list_num,inplace = True)\n",
    "job2.sort_values(by = 'companySize',ascending = False,inplace = True)\n",
    "\n",
    "\n",
    "from matplotlib import gridspec  #将数据分成多份\n",
    "gs = gridspec.GridSpec(10,1)#------整张图片分成10份，即10行\n",
    "plt.subplot(gs[:8])#---子视图，占位置上面的8行\n",
    "\n",
    "plt.scatter(x = job2.salary,\n",
    "           y = job2.companySize,\n",
    "           c = job2.workYearNum.map(color_map),\n",
    "           s = job2.workYearNum*100,alpha=0.35)\n",
    "\n",
    "#再复制一遍，稍作修改，此时出现点中点的效果\n",
    "plt.scatter(x = job2.salary,\n",
    "           y = job2.companySize,\n",
    "           c = job2.workYearNum.map(color_map))\n",
    "\n",
    "\n",
    "plt.xticks(np.arange(0,161,step =20),\n",
    "          [str(i) +'k' for i in np.arange(1,161,20)])\n",
    "plt.grid(axis = 'x')\n",
    "plt.box(False) #不要边框\n",
    "plt.title(label='   不同规模公司招聘要求    ',fontsize = 20,pad =30)\n",
    "\n",
    "plt.subplot(gs[-1])#最后一行\n",
    "x = np.arange(1,6)[::-1]\n",
    "y = np.zeros(5)\n",
    "\n",
    "plt.scatter(x,y,c = color_map.values(),s = x*100,\n",
    "           alpha=0.35)\n",
    "plt.scatter(x,y,c = color_map.values())\n",
    "plt.yticks([0],['经验'])\n",
    "plt.xticks(x,workYear_map.keys())\n",
    "plt.box(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "parental-cache",
   "metadata": {},
   "source": [
    "### category排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "patent-breast",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'companySize'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-216-53c4ec959b15>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mlist_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'2000人以上'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'200-2000人'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'150-500人'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'50-150人'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'15-50人'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'少于15人'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m#reorder 重新排序\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mjob2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompanySize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompanySize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompanySize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreorder_catefories\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_num\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mjob2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'companySize'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mascending\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5065\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5066\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5067\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'companySize'"
     ]
    }
   ],
   "source": [
    "'''一下代码写到上面\n",
    "\n",
    "#将str类型转换成类别类型，特殊的数据类型，再进行排序\n",
    "job2['companySize'] = job2['companySize'].astype('category')\n",
    "job2.dtypes\n",
    "#顺序\n",
    "list_num = ['2000人以上','200-2000人','150-500人','50-150人','15-50人','少于15人']\n",
    "#reorder 重新排序\n",
    "job2.companySize.companySize.companySize.cat.reorder_catefories(list_num,inplace = True)\n",
    "job2.sort_values(by = 'companySize',ascending = False,inplace = True)\n",
    "\n",
    "\n",
    "'''"
   ]
  },
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   "id": "governing-settlement",
   "metadata": {},
   "outputs": [],
   "source": []
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